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Curso completo de álgebra lineal de cero a experto
Udemy · 59.99 USD · ⭐ 4.48 · ⏱ 41,5 h · Español
Conoce toda el álgebra lineal de la mano de Juan Gabriel Gomila y María Santos. Asienta las bases para convertirte en el Data Scientist del futuro con todo el contenido del curso. En particular verás los mismos contenidos que explicamos en primero de carrera a matemáticos, ingenieros o informáticos como por ejemplo: • Logística e instalación de R y RStudio, Anaconda Navigator para Python y Octave GNU para ingeniería. • Cómo usar R, Python y Octave como si fuese una calculadora científica para complementar tu estudio día a día • Introducción a la programación funcional, creando funciones con R, Python y Octave para resolver tus problemas del álgebra lineal y que te servirá para seguir tomando a posteriori cursos de estadística, análisis de datos, machine learning e inteligencia artificial • Fundamentos esenciales de matemáticas incluyendo el estudio de cuerpos, un repaso de trigonometría, el método de inducción o los números complejos entre otros. • Matrices, sistemas de ecuaciones lineales, determinantes o el método de factorización LU que se utiliza en algoritmos que involucran el cálculo de matrices inversas o la resolución de sistemas de ecuaciones en redes neuronales o SVM • Vectores y operaciones vectoriales incluyendo combinaciones lineales, producto escalar, proyecciones ortogonales, producto vectorial y producto mixto que aparecen constantemente en el mundo de la física, la ingeniería y la programación. • El mundo de los espacios vectoriales donde se ubican los datos de un análisis incluyendo el cálculo de la dimensión, la búsqueda de una base, el cambio de base o el método de ortogonalización de Gram Smith. • Creación de espacios vectoriales como la suma directa, el producto o el espacio cociente. • Morfismos y aplicaciones lineales para clasificar y relacionar espacios vectoriales. • Diagonalización de los endomorfismos con el método de los valores y los vectores propios. Aplicación de la diagonalización a la transmisión de datos y al Machine Learning con Cadenas de Markov o ACP • Optimización lineal con el método del Simplex. • Repositorio Github con todo el material del curso para disponer de los mismos scripts que usamos en clase desde el minuto inicial. Una vez termines el curso podrás seguir con los mejores cursos de análisis de datos, machine learning e inteligencia aritificial publicados por Juan Gabriel Gomila como los cursos de Machine Learning o Inteligencia Artificial con Python o RStudio o el Curso de Data Science con Tidyverse y RStudio o el avanzado de TensorFlow. Todo el material del curso está enfocado en resolver los problemas de falta de base que presentan los estudiantes de esos cursos avanzados y poderlo hacer en un curso a parte te permitirá nivelar tus conocimientos y tomar los otros cursos con garantías de éxito.
Modelos predictivos con Machine Learning
Coursera · Español
Este curso tiene como objetivo proporcionar a los estudiantes una comprensión integral de los modelos predictivos utilizando técnicas de Machine Learning. Los participantes aprenderán a implementar, evaluar y mejorar modelos predictivos aplicables a diversas problemáticas reales, utilizando herramientas como Python y bibliotecas especializadas.
Curso de Gestión de Proyectos Ágiles. SCRUM y Kanban
Udemy · 44.99 USD · ⭐ 4.48 · ⏱ 10,5 h · Español
Este curso está dirigido para todos aquellos desarrolladores y profesionales de TI que están interesados en trabajar en equipos que usan metodologías ágiles. En este curso podremos ver por qué hoy en día las metodologías ágiles están en auge frente a las metodologías tradiciones de gestión de proyectos, cuales son sus ventajas y de que elementos se componen. Veremos las principales metodologías agiles como son el Framework Scrum y el Enfoque Kanban. Veremos los diferentes elementos necesarios para llevar a cabo el proceso completo de ejecución de un proyecto ágil: planificación, reuniones diarias, sprints, planificaciones... Este curso proporciona un enfoque teórico con varios casos de estudio que le ayudarán a afianzar los conocimientos adquiridos. Al finalizar este curso aprenderás: • Aprenderás las funciones y responsabilidades existentes en las metodologías Agile • Aprenderás la estimación en las metodologías ágiles • Aprenderás a gestionar los recursos en entornos de trabajo en constante cambio • Aprenderás a optimizar el tiempo de trabajo efectivo en los proyectos de desarrollo • Aprenderás a reducir los desperdicios en el desarrollo de software • Aprenderás a planificar y ejecutar proyectos ágiles • Aprenderás los principios ágiles, de Scrum y Kanban • Aprenderás a usar los diferentes eventos, artefactos y ceremonias de Scrum Este curso comienza explicando la Gestión de Proyectos tradicionales frente a la irrupción de las metologías ágiles como las metodologías con mayor implantación actualmente en el mundo empresarial. A continuación veremos en que consisten las metodologías ágiles junto con sus ventajas y beneficios para después continuar viendo sus dos principales exponentes, como son el Framework Scrum y el Enfoque Kanban, los cuales veremos en mayor detalle. Finalmente veremos como podemos aplicar las metodologías ágiles en los equipos de trabajo tanto locales como remotos. A mayores se incluyen las presentaciones de la toda la teoría vista a lo largo del curso y varios casos de estudio sencillos para mejorar la comprensión de los conceptos vistos a lo largo del curso. Si eres desarrollador y/o profesional TI y quieres conocer en que consisten las metodologías ágiles, especialmente Scrum y Kanban no dudes en inscribirte en este curso. ¡Nuevo Módulo Avanzado Agregado! Gestión de Proyectos con IA: Ejercicios Prácticos He añadido un módulo de vanguardia que transforma este curso en una experiencia práctica alineada con las tendencias de gestión de proyectos más actuales: • Aprenderás por qué la IA y Agile son la combinación perfecta para la optimización de proyectos. • Descubrirás cómo la IA potencia a Scrum y Kanban, desde el refinamiento de backlogs hasta el análisis de flujo. • Verás cómo implementar estas herramientas en tu equipo para liderar el futuro de la gestión ágil. Tu Experiencia Práctica: Ejercicios prácticos con aplicaciones impulsadas por Inteligencia Artificial El valor diferencial de esta nueva sección reside en los ejercicios prácticos propuestos a través de 7 Tareas Prácticas y 2 Simulaciones de Roles (Role Plays) que te obligarán a aplicar la teoría: • La "Suite de Gestión de Proyectos con IA": Recibirás el código fuente completo de esta aplicación en Python/Streamlit. La usarás para: • Generar una WBS completa, cronogramas y rutas críticas con IA. • Realizar Análisis de Riesgos proactivos. • Generar Documentación profesional (Actas, Definción de Alcance, Historias de Usuario, Informes Ejecutivos) en un solo clic. • Simuladores de Roles: Te enfrentarás a escenarios de la vida real: • Simulador SCRUM: Liderarás un equipo de IA a través de un sprint completo, gestionando eventos inesperados. • Simulador Kanban: Demostrarás con datos cómo limitar el WIP acelera el flujo. • Role Play de Liderazgo: Participarás en simulaciones interactivas (Role Plays) para practicar la Comunicación de Crisis con la dirección y la Facilitación de Retrospectivas para resolver problemas de proceso. ¡¡NUEVA ACTUALIZACIÓN!! 6 nuevas secciones completas sobre Gestión de Proyectos Inteligentes con Inteligencia Artificial El mundo de la gestión de proyectos está cambiando más rápido que nunca, y este curso también. En esta gran actualización de 2026 hemos incorporado más de 27 nuevas clases, 12 role plays y 6 cuestionarios de evaluación organizados en 6 módulos especializados que te convertirán en un Project Manager preparado para liderar en la era de la IA. Esto es lo que encontrarás en el nuevo contenido: • Módulo 1 – Fundamentos de la Gestión de Proyectos IA Aumentada: Descubre la convergencia entre Agile y la Inteligencia Artificial, los tipos de IA aplicables a la gestión de proyectos y un marco ético práctico para el PM aumentado. Incluye el polémico Manifiesto del Fracaso y el Juramento Hipocrático del PM de IA. • Módulo 2 – Planificación y Estimación Inteligente: Aprende a refinar tu backlog con IA generativa, domina la estimación predictiva, el roadmapping dinámico y los fundamentos estadísticos del pronóstico probabilístico. Incluye frameworks avanzados de priorización para portafolios de IA. • Módulo 3 – Ejecución de Sprints y Monitoreo Proactivo: Optimiza el flujo de trabajo con asignación inteligente de tareas, aplica análisis predictivo al monitoreo de sprints y aprende a gestionar riesgos antes de que ocurran. Incluye un manual de campo para los desastres más comunes en proyectos de IA. • Módulo 4 – Comunicación y Colaboración Aumentada por IA: Automatiza reportes, interpreta el sentimiento de tu equipo y aprende a traducir conceptos técnicos de IA a cualquier stakeholder. Incluye estrategias para cultivar equipos de alto rendimiento en entornos inciertos. • Módulo 5 – Retrospectivas Basadas en Datos y Mejora Continua: Extrae insights accionables de tus proyectos, genera planes de mejora con IA y construye el cerebro organizacional de tu equipo. Incluye formatos avanzados de retrospectiva diseñados específicamente para equipos de IA. • Módulo 6 – Liderazgo en la Era de la Gestión Ágil Inteligente: Descubre el futuro rol del Project Manager como estratega, selecciona e implementa herramientas de IA para tu contexto y aprende a diseñar organizaciones adaptativas. Incluye un framework de competencias para líderes del mañana. Cada módulo incluye role plays realistas —desde justificar Agile ante un directivo escéptico hasta liderar la gestión del cambio— y un cuestionario de evaluación para consolidar tu aprendizaje. ¡¡Espero que este curso te ayude a mejorar personal y profesionalmente y te ayude a conseguir tus objetivos profesionales!!
Modern Robotics, Course 1: Foundations of Robot Motion
Coursera · ⏱ 12 h–20 h · Inglés
Do you want to know how robots work? Are you interested in robotics as a career? Are you willing to invest the effort to learn fundamental mathematical modeling techniques that are used in all subfields of robotics? If so, then the "Modern Robotics: Mechanics, Planning, and Control" specialization may be for you. This specialization, consisting of six short courses, is serious preparation for serious students who hope to work in the field of robotics or to undertake advanced study. It is not a sampler. In Course 1 of the specialization, Foundations of Robot Motion, you will learn fundamental material regarding robot configurations, for both serial robot mechanisms and robots with closed chains. You will learn about configuration space (C-space), degrees of freedom, C-space topology, implicit and explicit representations of configurations, and holonomic and nonholonomic constraints. You will also learn how to represent spatial velocities and forces as twists and wrenches. This material is at the core of the study of anything that moves (e.g., robots). This course follows the textbook "Modern Robotics: Mechanics, Planning, and Control" (Lynch and Park, Cambridge University Press 2017). You can purchase the book or use the free preprint pdf. You will build on a library of robotics software in the language of your choice (among Python, Mathematica, and MATLAB) and use the free cross-platform robot simulator V-REP, which allows you to work with state-of-the-art robots in the comfort of your own home and with zero financial investment.
Master: Robot Framework con Python Test QA (En Español)
Udemy · 54.99 USD · ⭐ 4.48 · ⏱ 25,5 h · Español
Es un framework de automatización de tests de desarrollo dirigido y de automatización de procesos robóticos, de código abierto. Está basado en Python, y nos permite testar aplicaciones distribuidas y muy diversas. El entorno que se ha creado está compuesto por múltiples librerías y herramientas ya construidas. 7 razones para usar Robot Framework en el testeo de aplicaciones. 1. Es Open Source, como decíamos, lo cual permite disfrutar de todas las ventajas de ese tipo de código. 2. Es muy sencillo de instalar, se puede hacer usando el administrador de paquetes estándar de Python, Pip. Puede instalarse desde la fuente, mediante una distribución JAR e incluso de forma manual. 3. Es independiente de la aplicación a testar y de la plataforma, lo cual hace que Robot Framework resulte muy atractivo. 4. Los usuarios no necesitan usar ningún lenguaje de programación para implementar tests y ejecutarlos. 5. Proporciona multitud de librerías para testear todo tipo de aplicaciones, como librerías de Android, librerías de bases de datos, etc 6. También proporciona una librería API para la creación de librerías de tests propias que pueden implementarse tanto en Java como en Python. 7. Incorpora una línea de comandos y ficheros de salida en formatos XML legibles, que luego se utilizarán para la construcción de ficheros de log en formato HTML y ficheros de informe, también en HTML, con una gran cantidad de información mostrada de una forma bastante sencilla, intuitiva y precisa. Está formado por cuatro módulos o capas bien diferenciadas que describiré a continuación, de forma ascendente en relación a su estructura: • Sistema bajo testeo: La parte más física de la pila de capas que tiene la arquitectura. En ella encontramos cada uno de los sistemas físicos, aplicaciones, entornos, etc., que vayan a ser testados, automatizados… • Capa de Tests, con sus herramientas y librerías. Esta capa se conecta con la inferior mediante las interfaces de sistema, y con la superior mediante una librería API de test. • Infraestructura de Robot Framework: se encuentra más arriba, en la pila. Esta parsea los datos de testeo e interactúa con la capa inferior. • Y por último, los datos de testeo.
Simulate with Monte Carlo
Coursera · ⏱ 3 h · Inglés
Ready to turn uncertainty into opportunity? This Short Course was created to help data analysts master Monte Carlo simulation techniques to quantify risk and forecast outcomes under uncertainty. By completing this course, you'll transform complex probabilistic scenarios into actionable insights. You'll build robust simulation models, conduct sensitivity analysis, and create probability distributions that inform critical business decisions with confidence. By the end of this course, you will be able to: Build Monte Carlo simulation models for project ROI analysis Evaluate sensitivity through tornado chart analysis Construct currency-exchange exposure simulations Determine optimal iteration counts for model convergence This course is unique because it combines theoretical foundations with hands-on Python implementation, providing both Excel-based and programmatic approaches to Monte Carlo modeling. To be successful in this project, you should have a background in basic statistics and Python programming.
Credit Scoring de Basico a Avanzado con R y Python
Udemy · 49.99 USD · ⭐ 4.48 · ⏱ 9 h · Español
El credit scoring es un sistema automático de calificación de créditos que permite a las entidades financieras determinar la probabilidad de pago de un préstamo Este sistema financiero se aplica para la cartera minorista: personas (créditos de consumo y créditos hipotecarios) y mypes (micro y pequeñas empresas); sin embargo, es importante mencionar que para la cartera no minorista (empresas medianas, grandes y corporativas), el sistema de calificación de créditos utilizado es el rating. El funcionamiento del credit scoring se basa en la estadística. Es decir, marcha a través de algoritmos que van variando según la data que procesan. Para ello, los bancos evalúan el comportamiento de su cartera de créditos para determinar las características o patrones comunes que se repiten con mayor frecuencia en los créditos impagos. En cuanto a la información que necesita el banco, esta puede ser dividida en dos bloques: aquella que no requiere documentación y que usualmente el banco ya la tiene, y aquella que si la necesita. En el primer grupo, se encuentra la siguiente información: • Datos personales del posible acreedor: edad, profesión, estado civil, domicilio, hijos, etc. • Vinculaciones financieras con los clientes: tarjetas contratadas, cuentas bancarias, etc. • Historial crediticio: revisión de préstamos anteriores ya cancelados por el cliente y aquellos que aún están vigentes. • Información de ficheros externos: lista de morosos (Infocorp). Adicionalmente, la entidad bancaria podría solicitar una serie de documentos que sustenten o complementen la información anterior. Por ejemplo: • Acreditación documental del correcto cumplimiento de los préstamos que el cliente tiene con otras entidades. • Ingresos de los intervinientes en la operación (boletas de pago). • Justificación de la finalidad del crédito: en préstamos a mypes (facturas, contratos de compra-venta u otros, etc.). • Documentación registral y tasación de bienes a hipotecar, de ser necesario. Esta data es procesada por un software que, tras analizar la información, arroja un número que representa la probabilidad teórica de que un cliente pague el préstamo en el tiempo previsto.
AI-Assisted Code Modernization
Coursera · ⏱ 7 h · Inglés
Launch into application modernization, one of enterprise software's most in-demand specialties, with a portfolio-ready capstone that proves you can run a legacy transformation end to end. This course is built for developers and students new to modernization: no prior experience with legacy systems or refactoring required. You'll learn the practices that turn AI assistance into reliable, auditable, production-grade work — structured codebase navigation, disciplined refactoring, technical-debt reduction, and the five-stage modernization lifecycle (assess, plan, execute, validate, document). These skills transfer across Java, Python, JavaScript, TypeScript, .NET, COBOL, and mixed-language codebases. Developers, architects, QA leads, DevOps and platform engineers, engineering managers, IT security, and compliance officers each find their role represented, alongside the governance that takes AI from pilot to organization-wide use. IBM Bob is your demonstration tool, with hands-on labs in a no-install Coursera environment. You'll leave with a showcase-ready capstone: a complete legacy modernization, run start to finish on a real codebase.
OpenShift 4.20 and Docker: From Zero to Production (2026)
Udemy · 19.99 USD · ⭐ 4.48 · ⏱ 6,5 h · Inglés
Hello! as part of this course we will be working with OpenShift 4.20.5. There are actually no special prerequisites for this course, apart from internet access and a willingness to learn :). All topics are explained as accurately as possible to avoid generating any additional questions. But - if anything is unclear, requires further clarification, or if a new lecture needs to be added, you are more than welcome to leave a comment under the course or contact me directly via private message. All materials (scripts, deployed applications) are available in the resources under lectures, provided as links to GitLab repositories. There are two ways to work with OpenShift during the course: • OpenShift Sandbox, which does not require installing the entire cluster infrastructure on your local machine • OpenShift Local, where we install the cluster locally on your computer — the system requirements for this option are discussed in the course Throughout the course, we focus on practical application deployment. We don’t just go through dry examples — we actually deploy real applications written in Python and Java. The applications themselves do not contain complex business logic, as the goal of this course is not to teach programming in these languages, but to learn how to work with OpenShift and containerized applications. During the learning process, questions will naturally arise, and some topics discussed at a given moment may be difficult to understand. Don’t worry about this — throughout the course, certain topics are intentionally skipped (with a clear note that they will be covered later) in order to focus on other elements first. In the end, everything should come together into a coherent whole and give you the ability to navigate and work confidently with OpenShift.
Unlock Multimodal Search
Coursera · ⏱ 1,5 h · Inglés
"Unlock Multimodal Search" is an intermediate, hands-on course for developers and ML engineers ready to build the next generation of AI-powered search. Text-only search is no longer enough; this 90-minute course will teach you how to create applications that can search across different data types, such as finding text from an image. Using the powerful open-source vector database Weaviate, you will move from theory to a functioning demonstration. This course requires basic skills in Docker, APIs, Python, and the command line (CLI). Familiarity with vector databases. Docker Desktop must be installed. This course is focused on execution. You will learn to configure a Weaviate schema to handle both image and text embeddings for a single object, ingest multimodal data, and perform powerful cross-modal queries. Through a final, hands-on project that mirrors a real-world job task, you will not only build an image-to-text search demo but also learn how to measure its accuracy with precision metrics. By the end, you'll be equipped to architect and validate sophisticated, multimodal AI applications.
Machine Learning A-Z [2026]: ML, DL, AI with AWS, Python & R
Udemy · 159.99 USD · ⭐ 4.47 · ⏱ 49,5 h · Inglés
Interested in the field of Machine Learning? Then this course is for you! This course has been designed by two AI & Machine Learning experts so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. This course can be completed by doing either the AWS tutorials, Python tutorials, or R tutorials, or the three of them - AWS, Python & R. Pick the ones you need for your career. This course is fun and exciting, and at the same time, we dive deep into Machine Learning. It is structured the following way: • Part 1 - Data Preprocessing: Importing the dataset with pandas, Matrix of Features and Target Vector, Training & Test Sets, Imputing Missing Data, Encoding Categorical Variables, Feature Scaling • Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression • Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification • Part 4 - Clustering: K-Means, Hierarchical Clustering • Part 5 - Association Rule Learning: Apriori, Eclat • Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling • Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP • Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks • Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA • Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost • Part 11 - ML Data Preprocessing with AWS: Data types (Apache Parquet, JSON, CSV), Data Preparation with S3, ETL with AWS Glue, Data Wrangling with AWS Glue DataBrew & SageMaker Data Wrangler, Feature Engineering with SageMaker • Part 12 - ML Model Development with AWS: XGBoost, LightGBM, CatBoost, Ensemble Models, Hyperparameter Tuning Techniques, Building Ensemble Models for Regression & Classification with Amazon SageMaker AI, Natural Language Processing with Amazon Comprehend, Computer Vision with Amazon Rekognition, Text to Speech with Amazon Polly, Speech To Text with Amazon Transcribe, Text Extraction with Amazon Textract, Machine Translation with Amazon Translate • Part 13 - ML Model Deployment with AWS: Methods for Deploying Models in Production, Deployment in Amazon SageMaker AI, Serverless vs. Real-Time vs. Asynchronous Inference, Deployment Endpoints in Amazon SageMaker, SageMaker vs. ECS vs. EKS vs. Lambda Deployment Targets, CloudFormation & Cloud Development Kit (CDK), Elastic Container Registry (ECR), Elastic Container Service (ECS) & Fargate, Building Containers with Amazon ECR, ECS & EKS • Part 14 - ML Workflow Automation (CI/CD Pipelines) with AWS: AWS CodePipeline, AWS CodeBuild, AWS CodeCommit, AWS CodeDeploy, Creating an ML pipeline with Amazon SageMaker Pipelines • Part 15 - ML Solution Monitoring and Maintenance with AWS: Features of Responsible AI, Legal Risks of Generative AI, Tools for Responsible ML, Model/Data Quality and Bias Drift with SageMaker Clarify, Monitoring Models in Production with SageMaker Model Monitor, SageMaker Model Cards, SageMaker Inference Recommender, SageMaker Savings Plans Each section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and learn what you need for your career right now. Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models. And last but not least, this course includes both Python and R code templates which you can download and use on your own projects.
Python Scripting Case Study - Creating a Console Application
Coursera · Inglés
Develop practical Python scripting skills by building a complete console-based student management system from the ground up. In this hands-on course, you will apply core Python programming concepts to construct a menu-driven application that manages student records through Create, Read, Update, and Delete (CRUD) operations. Designed for learners with basic Python knowledge, this course guides you through organizing program flow with functions and loops, managing dynamic lists, handling user input, and implementing regular expression (regex) validation. You will also debug common runtime issues and evaluate application behavior through iterative testing to strengthen your problem-solving skills. Using a real-world case study, the course emphasizes learning by building. Each lesson progressively expands your application, helping you connect fundamental Python scripting concepts with practical software development tasks. By the end of the course, you will have constructed an intermediate-level console application and gained the confidence to develop similar interactive Python programs for real-world scenarios. Whether you want to strengthen your Python scripting, improve input validation techniques, or build reusable console applications, this course provides a structured, project-based learning experience focused on practical implementation.
The Agentic AI Engineering Masterclass 2026
Udemy · 19.99 USD · ⭐ 4.47 · ⏱ 13,5 h · Inglés
In this hands-on masterclass, you’ll learn how to design, build, and deploy next-generation AI agents that combine memory, tools, collaboration, and automation to solve real-world problems. Starting with the OpenAI Agents SDK, you’ll explore how to create simple agents and gradually extend them with advanced features such as persistent memory, guardrails, and smooth handoffs between workflows. You’ll then dive into multi-agent systems, where specialized agents, like researchers, analysts, and writers, work together, passing context and outputs to build complex deliverables. Along the way, you’ll learn how to orchestrate these systems with manager functions, enforce ethical and domain boundaries with guardrails, and design creative pipelines for use cases from market research to advertising campaigns. The course introduces multiple frameworks for building production-ready agentic workflows. You’ll explore AutoGen for multi-model collaboration, LangGraph for modular pipelines connected to user interfaces, and CrewAI for advanced orchestration. You’ll also learn how to extend agents with custom tools, from Python code execution for data analysis to classical machine learning models like linear regression, random forest, and XGBoost. You’ll gain practical experience with the Model Context Protocol (MCP), enabling agents to interoperate with standardized external services, and learn how to build and deploy MCP tools using Gradio. Finally, you’ll see how low-code platforms like n8n can bring everything together into seamless automation flows, integrating Gmail, Google Sheets, Google Calendar, and AI models to create complete end-to-end systems. By the end of the course, you’ll have the skills to: • Build AI agents with memory, tools, and reasoning capabilities. • Orchestrate multi-agent workflows for research, analysis, and creative tasks. • Integrate guardrails, handoffs, and oversight to ensure safe, reliable outputs. • Deploy advanced agentic workflows across AutoGen, LangGraph, CrewAI, and MCP. • Automate business processes with low-code tools like n8n connected to real-world apps. Whether you’re a developer, data scientist, or business innovator, this course equips you with the full toolkit to design AI systems that collaborate, automate, and scale in production.
The Fundamental of Data-Driven Investment
Coursera · ⏱ 12 h–20 h · Inglés
In this course, the instructor will discuss the fundamental analysis of investment using R programming. The course will cover investment analysis topics, but at the same time, make you practice it using R programming. This course's focus is to train you to do the elemental analysis for investment management that you might need to do in your job every day. Additionally, the study note to do using Python programming will be provided. The course is designed with the assumption that most students already have a little bit of knowledge in financial economics. Students are expected to have heard about stocks and bonds and balance sheets, earnings, etc., and know the introductory statistics level, such as mean, median, distribution, regression, etc. The instructor will explain the detail of R programming for beginners. It will be an excellent course for you to improve your programming skills. If you are very good at R programming, it will provide you an excellent opportunity to practice again with finance and investment examples. Professor Youngju Nielsen creates the course with the assistants of Keonwoo Lim and Jeeun Yuen. =========================================================================================== Coursera Course recommendations before this course for those who are not familiar with basic R programming: <Getting Started with R> https://www.coursera.org/projects/getting-started-with-r <Introduction to Business Analytics with R> https://www.coursera.org/learn/business-analytics-r <Statistics with Python > https://www.coursera.org/specializations/statistics-with-python
Claude Code Beginner Crash Course: Claude Code In a Day
Udemy · 19.99 USD · ⭐ 4.47 · ⏱ 10,5 h · Inglés
This course contains the use of artificial intelligence :) Welcome to the the Claude Code Beginner Crash Course! This curriculum is designed for professionals new to Claude Code and assumes you have a solid background in software engineering and are proficient in Python, Next JS and generative ai. We will be working extensively in the terminal and using the Cursor IDE for seamless integration, but you can follow along with any editor that supports the Claude Code extension. This course is for software developers, AI engineers, and data scientists who want to move beyond simple chat interactions and harness the full power of Claude Code to build automated, context-aware, and secure development workflows. What You Will Learn in Claude Code Claude Code is more than just a coding assistant; it's a powerful, extensible platform for creating AI-driven development tools. This course will teach you to master its core architecture, from basic commands to building complex, multi-agent systems. Architecture and Core Concepts • Slash Commands: Go beyond basic prompting and learn to control every aspect of Claude Code's behavior, context, and configuration directly from your terminal. • Persistent Memory (CLAUDE .md): Learn to give Claude a long-term memory. We'll cover how to create and manage user, project, and local memory files to store preferences, coding standards, and architectural context that persists across sessions. • Hooks: Unlock the ultimate automation tool. You will learn to create shell commands that trigger at specific events in Claude's lifecycle, enabling you to automate everything from running tests to formatting code and even calling other AI agents. • Sub-Agents: Design and build a team of specialized AI assistants. You'll learn how to create and manage sub-agents, each with its own unique context, tools, and system prompt, to handle specific tasks like code reviews, debugging, or security analysis. • Agentic Coding Principles • Claude Code Plugins • Claude Code Output Styles • Course syllabus covers: Claude Certified Architect - Foundations Key Capabilities and Topics Covered This course provides a deep dive into practical, real-world applications of Claude Code: • Advanced Context Engineering: Master the art of keeping Claude focused and efficient by managing its context window with /clear, /compact, and automated memory systems. • Multi-Agent Workflows: Design and implement complex workflows where specialized sub-agents collaborate to solve problems, orchestrated by Hooks and a central Claude instance. • Tool Use and Permissions: Securely grant Claude access to your local tools and scripts, and manage what it can and cannot do with granular permission controls. • IDE Integration: Set up and optimize the Claude Code extension in your IDE for a seamless workflow that combines terminal power with in-editor diffs and context. • Cost and Performance Optimization: Understand the tokenomics of Claude Code and learn best practices for managing cost and ensuring high-performance interactions. • Real-World Projects: Apply everything you've learned in hands-on projects, from building a custom linter with Hooks to creating a multi-agent system for automated code reviews. Who this course is for: • Advanced GenAI Users: Users who are ready to go beyond the chat interface and leverage the full programmatic power of Claude Code. • AI Engineers: Professionals looking to build and deploy sophisticated, agentic workflows directly within their development environment. • Application Developers: Software engineers who want to automate repetitive tasks and integrate a deeply context-aware AI partner into their daily workflow. • Data Scientists: Practitioners aiming to automate complex data analysis, scripting, and pipeline generation tasks.
Power BI for Data Science and Analytics
Coursera · ⏱ 15 h · Inglés
Welcome to Power BI for Data Science and Analytics. This course is designed to help you develop practical skills in data analytics using Power BI, a leading business intelligence platform. You will learn to use key Power BI features to extract and visualize insights from your data, making it easier to share findings and drive decisions. By the end of this course, you’ll be able to: - Use Power BI effectively for data analytics from connecting to data sources to building interactive reports and dashboards. - Create insightful visualizations that communicate important trends and patterns clearly to your team or stakeholders. - Apply AI features in Power BI such as natural language queries, anomaly detection, and cognitive services—to uncover deeper insights from your data. - Centralize and model data with dataflows, enabling reusable, efficient reporting for both text and image analytics. - Evaluate the impact of AI features on your analysis, so you can select the right tools for your projects. - Design scorecards to track KPIs and performance metrics in a format that’s easy to understand and act upon. This course is built for Data Analysts, Business Intelligence Developers, and Business Analysts looking to strengthen their analytical and reporting skills using Power BI. Some familiarity with MS Excel, Python, or Power BI is helpful, but not required, you willl gain hands-on experience with practical assignments and real-world scenarios. Join us on this journey to elevate your Power BI analytical skills and design capabilities.
Introduction to Databricks in 1 Hour
Udemy · 19.99 USD · ⭐ 4.47 · ⏱ 1,5 h · Inglés
Welcome to Introduction to Databricks, your practical, beginner-friendly guide to one of the most in-demand data platforms in the industry today. My name is Dimitar Shtev, and I’ve spent nearly a decade working with data across analytics, engineering, and machine learning. In this course, I will walk you step-by-step through everything you need to confidently start using Databricks—even if you’ve never worked with it before. We begin by exploring what Databricks is, why organizations rely on it, and how its major components fit together. You’ll learn about the Data Lakehouse, Delta Lake, SQL, Databricks Runtime, and the architecture that powers scalable data processing. From there, we move into hands-on learning using Databricks’ free-for-life community edition. You will run Python scripts, configure compute resources, organize your workspace, load CSV files, and build interactive visualizations and dashboards. Finally, we put everything into practice with a real machine learning project: You will collect real Bitcoin price data, prepare it for modeling, convert SQL tables into PySpark objects, train a regression model, and display future price predictions inside Databricks. Whether you’re a data analyst, data engineer, BI professional, developer, or aspiring data scientist, Databricks is becoming an essential tool—and this course gives you the foundations AND the confidence to use it in real-world scenarios. By the end, you’ll have a functional Databricks environment, hands-on experience, and practical workflows you can apply at work the very next day.
파이썬의 데이터 과학 소개
Coursera · Coreano
이 과정에서는 학습자에게 람다, csv 파일 읽기 및 조작, numpy 라이브러리와 같은 기본적인 파이썬 프로그래밍 기술을 포함하여 파이썬 프로그래밍 환경의 기본 사항을 소개합니다. 이 과정에서는 인기 있는 python pandas 데이터 과학 라이브러리를 사용하여 데이터 조작 및 정리 기술을 소개하고 데이터 분석을 위한 중심 데이터 구조로 Series 및 DataFrame의 추상화를 소개하고 groupby, merge 및 pivot 테이블 같은 함수를 효과적으로 사용하는 방법에 대한 튜토리얼을 제공합니다. 이 과정이 끝나면 학생들은 표 형식의 데이터를 가져와 정리하고 조작하고 기본 추론 통계 분석을 실행할 수 있습니다. 이 과정은 다른 ‘파이썬을 사용한 응용 데이터 과학’ 과정보다 먼저 수강해야 합니다. 파이썬의 응용 플로팅, 차트 및 데이터 표현, 파이썬의 응용 머신 러닝, 파이썬의 응용 텍스트 마이닝, 파이썬의 응용 소셜 네트워크 분석.
Datadog Monitoring - A Full Basic to ADVANCE Datadog guide
Udemy · 34.99 USD · ⭐ 4.47 · ⏱ 11,5 h · Inglés
"Datadog is an observability service for cloud-scale applications, providing monitoring of servers, databases, tools, and services, through a SaaS-based data analytics platform." What's included in the course ? • Complete Datadog monitoring tool's features explained from Scratch to In-depth Advance level. • Covers a wide scope of almost all the Datadog features listed in Datadog's official documentation. • Datadog Agent setup & configuration, Metrics, Events, Infrastructure Monitoring, Log Management, Application Performance Monitoring, Continuous Profiling, UI Monitoring, Dashboards, Notebooks, Monitors and many more. • Each Datadog monitoring feature and the associated concepts are demonstrated with proper Theory and Real-time Hands-On examples. • Interact with Multiple integrations supported by Datadog monitoring tool. • Build and Instrument a Python Flask application for Application Performance Monitoring and Continuous Profiling. • Exclusive Datadog Topics like End-to-End Log Pipeline (LMS), Trace Pipeline (APM), Datadog Containerized Agent, Application Instrumentation etc. • This course is useful for Application developers too as there are multiple sections in the course that involves Instrumenting Applications to generate Custom Metrics, Custom Events, Traces etc. • For Datadog Administrators, there is a section for Datadog Account Management that involves Granting Roles & Permissions, API keys, SAML Group mappings, Audit Trail, Sensitive Data Scanner etc. • Course is packed with many Datadog Tips & Tricks that can be very helpful in your day to day interaction with Datadog monitoring tool in Real projects. After completing this Datadog course, you can start working on any Real-time Datadog monitoring project with full confidence. Please go through the above Course content/Curriculum to get more details of it.
Fundamentals of Computer Organization
Coursera · Inglés
Whether you're a curious programmer, an aspiring hardware engineer, or simply someone who wants to understand the technology that powers the world, this course will provide you with a fundamental and practical understanding of computer architecture. This course goes beyond surface-level programming to reveal the core architectural principles that determine a computer's speed and efficiency. You'll learn how a CPU executes a program, from the simple, one-step process to the high-speed efficiency of a 5-stage pipeline. You will also learn how to overcome performance barriers. Upon completion, one will be able to: - Discuss the significance of latches and clocks in circuit design. - Explain how instructions are executed in a single-cycle and a 5-stage pipelined design. - Identify and solve structural, data, and control pipelining hazards. - Demonstrate a deep understanding of branch prediction. - Describe the fundamental principles of an out-of-order processor implementation. - Elucidate how temporal and spatial localities affect cache performance. This is an intermediate-level course, intended for learners with a background in computer science or electronics engineering. To succeed in this course, you should have experience with a high-level programming language like C, C++, or Python.
AI & Python Development Megaclass - 300+ Hands-on Projects
Udemy · 19.99 USD · ⭐ 4.47 · ⏱ 58 h · Inglés
Dive into the ultimate AI and Python Development Bootcamp designed for beginners and aspiring AI engineers. This comprehensive course takes you from zero programming experience to mastering Python, machine learning, deep learning, and AI-powered applications through 100 real-world projects. Whether you want to start a career in AI, enhance your development skills, or create cutting-edge automation tools, this course provides hands-on experience with practical implementations.(AI) You will begin by learning Python from scratch, covering everything from basic syntax to advanced functions. As you progress, you will explore data science techniques, data visualization, and preprocessing to prepare datasets for AI models. The course then introduces machine learning algorithms, teaching you how to build predictive models, analyze patterns, and make AI-driven decisions. You will work with TensorFlow, PyTorch, OpenCV, and Scikit-Learn to create AI applications that process text, images, and structured data. As you advance, you will develop chatbots, recommendation systems, sentiment analyzers, and automation tools using real-world datasets. You will gain expertise in natural language processing (NLP), computer vision, and reinforcement learning, mastering how AI is applied in various industries. The course also covers AI ethics, model optimization, and deployment strategies, ensuring you understand how to scale AI projects efficiently. By the end of the course, you will have 100 hands-on projects that demonstrate your skills in AI development, automation, and machine learning. Whether you’re looking to launch an AI-driven startup, enhance your resume with in-demand AI skills, or automate business processes, this course equips you with everything you need. Join now and become proficient in Python and AI development, unlocking endless opportunities in the tech industry.
Big Data Analysis with Scala and Spark
Coursera · Inglés
Manipulating big data distributed over a cluster using functional concepts is rampant in industry, and is arguably one of the first widespread industrial uses of functional ideas. This is evidenced by the popularity of MapReduce and Hadoop, and most recently Apache Spark, a fast, in-memory distributed collections framework written in Scala. In this course, we'll see how the data parallel paradigm can be extended to the distributed case, using Spark throughout. We'll cover Spark's programming model in detail, being careful to understand how and when it differs from familiar programming models, like shared-memory parallel collections or sequential Scala collections. Through hands-on examples in Spark and Scala, we'll learn when important issues related to distribution like latency and network communication should be considered and how they can be addressed effectively for improved performance. Learning Outcomes. By the end of this course you will be able to: - read data from persistent storage and load it into Apache Spark, - manipulate data with Spark and Scala, - express algorithms for data analysis in a functional style, - recognize how to avoid shuffles and recomputation in Spark, Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line. This course is intended to be taken after Parallel Programming: https://www.coursera.org/learn/parprog1.
DevOps Interview Masterclass: 60+ In-Depth Question & Answer
Udemy · 69.99 USD · ⭐ 4.47 · ⏱ 6,5 h · Inglés
Unlock Your DevOps Interview Success: Master Essential Questions! Are you ready to ace your DevOps interviews? If you've just wrapped up your DevOps course and are gearing up for those critical interview rounds, this course is your golden ticket! Puzzled About What to Expect? Dive into the minds of interviewers with our carefully selected 50 Plus DevOps interview questions. These aren't just any questions; they're the key to unlocking what interviewers really want to know from you Discover Your Strengths and Fine-Tune Your Knowledge: Unsure which DevOps domain you excel in? Our course doesn't just quiz you – it guides you in self-evaluation, helping you identify and leverage your strongest areas. Learn, Respond, Impress: We don't just list questions; we equip you with smart, impactful answers. Walk into your interviews with confidence, knowing you're prepared to handle whatever comes your way. Navigate Through the DevOps Seas with Confidence: From AWS to Docker, Terraform to Jenkins, and Kubernetes to Helm – we cover it all. Get a grip on tools like GitHub, Vault, Sonaqube, and Python, and master Linux and monitoring/alerting systems like Prometheus, Alert Manager, and Grafana. Boost Your Confidence: This course isn't just about learning answers; it's about building the confidence you need to shine in your interviews. We aim to leave you feeling ready and excited to showcase your skills. Join Our Interactive Journey: This isn't a one-way street. Join our interactive sessions, where we dissect each question, explore the nuances of effective responses, and prepare you to think on your feet. Your Opportunity to Stand Out Is Here: Say goodbye to interview jitters and hello to interview triumphs. Enroll in our DevOps interview mastery course today and turn your interview into a conversation about when you can start!
Project on Recommendation Engine - Book Recommender
Coursera · ⏱ 6 h–8 h · Inglés
Build a practical Book Recommendation Engine with Python while learning the core techniques behind modern recommender systems. In this hands-on, project-based course, you'll progress from understanding recommendation system fundamentals to designing and implementing a functional content-based recommendation engine using structured data and text features. You'll begin by exploring the objectives and architecture of a book recommender system, preparing datasets through preprocessing, and engineering metadata features to support user-driven filtering. Next, you'll develop content-based filtering models using TF-IDF, Count Vectorizers, and similarity scoring techniques. You'll also combine and transform multiple book attributes—including title, author, and genre—to improve recommendation relevance and generate more personalized results. This course is ideal for learners who want practical experience applying Python and data science techniques to recommendation systems. By following a complete end-to-end project, you'll gain experience preparing data, engineering features, building similarity frameworks, and refining recommendation outputs using structured and textual information. If you're looking to understand how content-based recommendation engines are designed and implemented through a real-world book recommendation project, this course provides a structured, practical learning experience from foundation to implementation.
Curso avanzado de estadística multivariante con R y Python
Udemy · 59.99 USD · ⭐ 4.47 · ⏱ 16,5 h · Español
Bienvenido al Curso avanzado de estadística multivariante con R y Python, donde encontrarás todo lo que necesitas para analizar datos del mundo multivariante tanto en R como en Python, los dos lenguajes de programación más utilizados de todo el mundo para el análisis de datos, y dos habilidades super solicitadas por las empresas en las ofertas de trabajo de Linkedin. Además el curso viene con un REGALO DE BIENVENIDA que se trata de un Libro completo de más de 160 páginas de contenido sobre Estadística Multivariante!!! Y tendrás acceso a un grupo privado para dudas para que no te sientas solo. ¿Qué vamos a aprender? • Empezaremos con el análisis exploratorio para ser capaces de describir y visualizar los datos de las muestras que tenemos. La visualización es importante para entender los datos, así que poder producir gráficos 2D y 3D es sin duda un requisito para poder hacer un correcto análisis multivariante. • Luego vamos a ver toda la teoría detrás de las variables aleatorias multivariantes y sus distribuciones respectivas. • También veremos los métodos de inferencia multivariante para obtener conclusiones más generales respecto al proceso que ha generado los datos de que disponemos y poder contrastar hipótesis respecto a su estructura. • Los métodos de componentes principales (PCA) y análisis factorial para reducir la dimensión y ver patrones que antes no se veían debido a la gran cantidad de datos, muy utilizados en el campo del Big Data para poder analizar datos en nuestras propias máquinas. • El escalado multidimensional (MDS) para cuando los datos disponibles no corresponden a variables sino a similitudes o semejanzas entre ellos. • Los métodos de agrupaciones que son clustering y clasificación para investigar si los elementos de nuestra muestra forman un grupo homogéneo o no, y poder clasificar nuevos datos que vengan en un futuro en uno de dichos grupos. • Y finalizaremos con los métodos de regresión lineal múltiple y regularización, para crear modelos de regresión cuando tenemos más de una variable explicativa. Y todo esto con transparencias explicando toda la teoría, ejemplos y casos prácticos explicados al completo, todo el código fuente tanto en R como en Python y por supuesto ejercicios prácticos, cuestionarios, tareas, y material complementario para poder seguir el curso sin ningún problema y que al completarlo seas un experto del análisis de datos multivariante.
App Deployment, Debugging, and Performance - 日本語版
Coursera · Japonés
アプリケーションデベロッパーは、このコースを通して、Google Cloud エコシステムのコンポーネントをシームレスに統合するクラウドネイティブなアプリケーションを設計、開発する方法を学びます。講義、デモ、ハンズオンラボを通して、インフラストラクチャをコードとして扱うことによって再現可能なデプロイを作成する方法、アプリケーションに適したアプリケーション実行環境を選択する方法、アプリケーションのパフォーマンスをモニタリングする方法を学習します。各ラボのいずれかのバージョンを完了する必要があります。各ラボは Node.js で利用できるほか、ほとんどの場合、Python または Java でも提供されます。各ラボはご希望の言語で完了できます。
Manim For Beginners: Create Stunning Animations With Code
Udemy · 29.99 USD · ⭐ 4.47 · ⏱ 3,5 h · Inglés
Key features: • Hands-on Learning: Dive into the fundamentals of Python animation with Manim through practical, step-by-step lessons. Gain confidence as I guide you through each concept, making it accessible and enjoyable for anyone eager to learn. • No Prior Experience Needed: Perfect for beginners, this course assumes no prior knowledge of animation or programming. • Manim Mastery: Unlock the full potential of the Manim library, a powerful tool for creating high-quality animations. Explore its features, functions, and applications to bring your creative vision to life. • Project-Based Learning: Apply your skills immediately by working on real-world projects. From basic shapes to intricate scenes, you'll build a strong foundation for creating dynamic and visually stunning animations. • Practical Tips and Strategies: Learn essential tips and strategies to enhance your animation workflow. Discover time-saving techniques and best practices that will elevate the quality of your animations and streamline your creative process. What You'll Learn: • Basics of Python programming with Manim for animation • Installation and setup of the Manim library • Creating simple animations with geometric shapes • Mastering advanced techniques for dynamic scenes • Integrating mathematical expressions into animations • Animating axes and plotting functions • Tips and tricks for optimizing and refining your creations Who Should Enroll: • Beginners with no coding or animation experience • Python enthusiasts looking to explore animation • Educators wanting to integrate interactive visuals into their teaching materials • Creatives interested in expressing ideas through animation Join me on this exciting journey into the world of animation. Enroll now and start creating captivating visuals with Python and Manim!
Concurrent and Parallel Programming in Python
Coursera · Inglés
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Dive into the world of concurrent and parallel programming with this detailed course designed for Python developers. Begin with threading, learning how to create and manage threads, and progress to building complex systems with threading classes. Explore practical projects like creating a Wikipedia reader and a Yahoo Finance reader, mastering the use of queues, schedulers, and database integration. Advance to multiprocessing, understanding how to leverage multiple CPU cores for enhanced performance. Learn to implement multiprocessing queues and pools, handle multiple arguments, and optimize CPU-intensive tasks. Finally, delve into asynchronous programming, mastering async tasks, timeouts, and libraries. Combine asynchronous and multiprocessing techniques for robust and scalable applications. This course provides a thorough understanding of concurrent and parallel programming, preparing you to tackle real-world challenges and optimize your Python applications for performance and efficiency. This course is ideal for Python developers, software engineers, and data scientists who want to enhance their skills in concurrent and parallel programming. A basic understanding of Python is recommended.
Python for Financial Analysis and Algorithmic Trading
Udemy · 109.99 USD · ⭐ 4.46 · ⏱ 16,5 h · Inglés
Welcome to Python for Financial Analysis and Algorithmic Trading! Are you interested in how people use Python to conduct rigorous financial analysis and pursue algorithmic trading, then this is the right course for you! This course will guide you through everything you need to know to use Python for Finance and Algorithmic Trading! We'll start off by learning the fundamentals of Python, and then proceed to learn about the various core libraries used in the Py-Finance Ecosystem, including jupyter, numpy, pandas, matplotlib, statsmodels, zipline, Quantopian, and much more! We'll cover the following topics used by financial professionals: • Python Fundamentals • NumPy for High Speed Numerical Processing • Pandas for Efficient Data Analysis • Matplotlib for Data Visualization • Using pandas-datareader and Quandl for data ingestion • Pandas Time Series Analysis Techniques • Stock Returns Analysis • Cumulative Daily Returns • Volatility and Securities Risk • EWMA (Exponentially Weighted Moving Average) • Statsmodels • ETS (Error-Trend-Seasonality) • ARIMA (Auto-regressive Integrated Moving Averages) • Auto Correlation Plots and Partial Auto Correlation Plots • Sharpe Ratio • Portfolio Allocation Optimization • Efficient Frontier and Markowitz Optimization • Types of Funds • Order Books • Short Selling • Capital Asset Pricing Model • Stock Splits and Dividends • Efficient Market Hypothesis • Algorithmic Trading with Quantopian • Futures Trading
Generative AI for Human Resource Professionals: Gen AI in HR
Udemy · 34.99 USD · ⭐ 4.46 · ⏱ 2,5 h · Inglés
If you’re a human resources professional who wants to move beyond theory and truly understand how Generative AI works inside HR functions, this course is designed for you. “Generative AI for Human Resource Professionals: Gen AI in HR” does not just show you prompts and tools. It takes you from understanding the current state of Generative AI to practically applying it across recruitment, employee engagement, and learning & development — while also addressing the critical risks of bias, hallucination, misuse, and data privacy. We begin by grounding you in the fundamentals. You will understand what Generative AI actually is, how large language models function at a high level, and how these technologies are already reshaping HR workflows globally. You will also see a basic demonstration of running Generative AI code in Python so that you develop technical awareness — even if you are not from a coding background. From there, the course moves into real HR applications. In Recruitment & Selection, you will learn how Generative AI can help create compelling job descriptions and social media postings, evaluate resumes systematically, assist in interview preparation, and support candidate interactions. Most importantly, you will also learn how to monitor AI-generated outputs to ensure quality, fairness, and compliance in the hiring process. In Employee Engagement, the course explores how AI can respond to employee queries related to HR policies using conversational systems. You will understand how Retrieval Augmented Generation (RAG) works — a powerful approach that allows AI systems to retrieve verified organizational documents before generating responses. A technical walkthrough is included to help you understand the architecture behind such systems. The Learning & Development section goes deeper into designing AI-driven learning paths, creating personalized training journeys, and integrating Generative AI agents within L&D portals. You will also examine the real-world challenges of deploying AI in training environments, including content accuracy, skill mapping, and adoption resistance. This course is not limited to theory. In the Practical Applications section, you will see demonstrations of running large language models locally and understand how enterprise tools such as Microsoft Copilot can be applied in HR scenarios. Finally, the course addresses the most critical aspect of AI adoption: responsible implementation. You will learn how to analyze user inputs for actionable workforce insights, reduce bias and hallucination in AI responses, prevent misuse of generative systems, and protect sensitive employee information when integrating AI into organizational processes. By the end of this course, you will be able to: • Apply Generative AI in recruitment, engagement, and learning functions • Understand how AI systems like RAG and LLMs function in HR use cases • Evaluate AI outputs for bias, hallucination, and compliance risks • Protect confidential HR data while deploying AI tools • Lead responsible AI implementation initiatives within your organization Generative AI is not just another HR technology trend. It is reshaping how HR creates value — from faster hiring decisions to personalized employee development at scale. This course equips you with both conceptual clarity and practical understanding so that you can confidently contribute to AI-led transformation in your organization. If you want to stay ahead in HR and understand both the opportunity and responsibility that comes with Generative AI, enroll now and take the next step toward building AI-enabled HR capability.
AI Engineer Bootcamp 2026: LLMs, RAG, AI Agents & Vector DBs
Udemy · 19.99 USD · ⭐ 4.46 · ⏱ 28 h · Inglés
Become a job-ready AI Engineer and master the skills companies expect in 2026: Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and vector databases. You will follow an AI-engineer roadmap from foundations to deployment, so you can design, build, and ship production-grade AI features instead of just calling APIs. This course is built for developers who want to transition into AI engineering roles and need a single, practical path that covers LLM concepts, RAG pipelines, agents, evaluation, and deployment. Every module ties skills directly to what AI engineer roadmaps and job descriptions list as must-have capabilities in 2026. What you'll be able to do as an AI Engineer • Understand and explain the AI engineer skill stack: LLMs, RAG, AI agents, evaluation, and deployment. • Build LLM-powered applications with modern APIs and frameworks, using patterns you can discuss in interviews. • Design and implement RAG pipelines with embeddings, vector databases, and retrieval strategies that ground models in real data. • Create AI agents that use tools, plan multi-step workflows, and interact with external APIs like a real product feature. • Evaluate and debug AI systems using practical metrics - accuracy, hallucinations, latency, reliability - that matter in production. • Deploy AI services and integrate them into web backends or existing products so your work looks production-ready on a CV and portfolio. Projects you'll add to your portfolio • An LLM-powered Q&A assistant grounded in your own documents using RAG and a vector database. • An AI agent that calls external tools and APIs to complete multi-step tasks, showcasing planning and tool-use. • A production-style AI microservice that exposes LLM and RAG functionality over an API, ready to plug into a real app. • Additional mini-projects that demonstrate prompt engineering, evaluation workflows, and AI-powered automation. You can reference these projects in interviews, GitHub, and LinkedIn to prove you can design and ship full AI workflows, not just toy demos. Who this course is for • Software Engineers and Backend or Full-Stack Developers targeting AI Engineer roles. • Data Scientists and ML Engineers who want to move into LLM and agent-centric work. • Career-switchers and motivated beginners who want a guided AI engineer roadmap instead of random tutorials. • Tech professionals who want to add AI Engineer skills to their current role and stand out in 2026. Requirements • Comfortable with basic Python (loops, functions, packages); some API experience is helpful but not required. • No prior deep learning experience necessary; we cover the essentials as they relate to AI engineer work. • A computer with internet access to run notebooks, call APIs, and connect to vector databases. If your goal is to apply for AI Engineer roles, talk confidently about RAG, agents, and vector databases, and ship projects that match modern roadmaps, this course is designed to get you there.
Complete Computer Vision Bootcamp With PyTorch & Tensorflow
Udemy · 19.99 USD · ⭐ 4.46 · ⏱ 59,5 h · Inglés
In this comprehensive course, you will master the fundamentals and advanced concepts of computer vision, focusing on Convolutional Neural Networks (CNN) and object detection models using TensorFlow and PyTorch. This course is designed to equip you with the skills required to build robust computer vision applications from scratch. What You Will Learn Throughout this course, you will gain expertise in: • Introduction to Computer Vision • Understanding image data and its structure. • Exploring pixel values, channels, and color spaces. • Learning about OpenCV for image manipulation and preprocessing. • Deep Learning Fundamentals for Computer Vision • Introduction to Neural Networks and Deep Learning concepts. • Understanding backpropagation and gradient descent. • Key concepts like activation functions, loss functions, and optimization techniques. • Convolutional Neural Networks (CNN) • Introduction to CNN architecture and its components. • Understanding convolution layers, pooling layers, and fully connected layers. • Implementing CNN models using TensorFlow and PyTorch. • Data Augmentation and Preprocessing • Techniques for improving model performance through data augmentation. • Using libraries like imgaug, Albumentations, and TensorFlow Data Pipeline. • Transfer Learning for Computer Vision • Utilizing pre-trained models such as ResNet, VGG, and EfficientNet. • Fine-tuning and optimizing transfer learning models. • Object Detection Models • Exploring object detection algorithms like: • YOLO (You Only Look Once) • Faster R-CNN • Implementing these models with TensorFlow and PyTorch. • Image Segmentation Techniques • Understanding semantic and instance segmentation. • Implementing U-Net and Mask R-CNN models. • Real-World Projects and Applications • Building practical computer vision projects such as: • Face detection and recognition system. • Real-time object detection with webcam integration. • Image classification pipelines with deployment. Who Should Enroll? This course is ideal for: • Beginners looking to start their computer vision journey. • Data scientists and ML engineers wanting to expand their skill set. • AI practitioners aiming to master object detection models. • Researchers exploring computer vision techniques for academic projects. • Professionals seeking practical experience in deploying CV models. Prerequisites Before enrolling, ensure you have: • Basic knowledge of Python programming. • Familiarity with fundamental machine learning concepts. • Basic understanding of linear algebra and calculus. Hands-on Learning with Real Projects This course emphasizes practical learning through hands-on projects. Each module includes coding exercises, project implementations, and real-world examples to ensure you gain valuable skills. By the end of this course, you will confidently build, train, and deploy computer vision models using TensorFlow and PyTorch. Whether you are a beginner or an experienced practitioner, this course will empower you with the expertise needed to excel in the field of computer vision. Enroll now and take your computer vision skills to the next level!
Curso práctico de Estadística y Probabilidades
Udemy · 59.99 USD · ⭐ 4.46 · ⏱ 16 h · Español
Este es un curso completamente práctico para aprender Estadística y Probabilidades a través de ejercicios resueltos paso a paso. Durante el curso verás que en cada tema en concreto tenemos una amplia variedad de ejercicios resueltos, explicados al detalle, recordando en cada paso el contenido de teoría sin necesidad de pasar antes por una clase o curso teórico de Estadística. El curso está especialmente dedicado a estudiantes universitarios, ya que estos son los que quizás están más interesados en este tipo de contenido cuando están pasando la asignatura de Estadística. Pero también muy útil para los que necesiten recordar estos conceptos, propiedades y métodos, o para aquellas personas que quieren dedicarse al mundo del Data Science y necesitan una base de estadística, probabilidades e inferencia, incluso para los programadores de R o Python que quieran recordar la base de Estadística para entender los métodos más avanzados de Machine Learning e Inteligencia Artificial. Además, podrás sugerir tú mismo los próximos ejercicios resueltos que subiremos para actualizar el curso. ¿Qué te parece la idea? Consideramos que esto es muy importante, primero para mantener actualizado el curso, y segundo, para que esté enfocado en las necesidades reales de los estudiantes. Durante el curso, en todas las secciones, encontrarás tareas para poner en práctica lo aprendido. Y al final del curso podrás auto-evaluarte con cuestionarios y con varios exámenes completos reales donde se mezclan todos los temas. Todo ello con su corresponiente solución. ¿Qué ventajas tiene este curso? • 100% práctico: ejercicios y soluciones con el iPad, con una excelente resolución. • Resúmenes de teoría, fórmulas y propiedades en cada sección. • Conceptos de teoría explicados en la práctica a través de los ejercicios resueltos. • Material en pdf con los ejercicios resueltos de manera escrita también. • Ejercicios de exámenes de universidad resueltos. • Acceso al grupo privado para dudas, te sentirás acompañado! • Podrás sugerir tú mismo los próximos ejercicios resueltos que subiremos para actualizar el curso. • Sorpresa al final. Incluye los temas: • Descriptiva univariante (media, mediana, varianza, histogramas, boxplots, etc.) • Descriptiva bivariante y regresión (covarianza, correlación, regresión lineal, etc.) • Probabilidades (probabilidad frecuentista, probabilidad condicional, teorema de Bayes, etc.) • Variable aleatoria (discreta y continua, valor esperado, varianza, etc.) • Distribuciones (Binomial, Poisson, Uniforme, Exponencial, Normal, etc.) Es una extensión del curso de Ejercicios resueltos de Estadística (gratuito) de 2h de duración. En este curso tenemos más de 12 horas y más de 100 ejercicios resueltos y se va a continuar actualizando todos los meses con ejercicios propuestos por los propios alumnos.
Agentic AI Development with Agent Framework, MCP and .NET
Udemy · 49.99 USD · ⭐ 4.46 · ⏱ 6 h · Inglés
"You're a Senior Architect, but AI makes you feel like a Junior again." If you have tried to learn Agentic AI, you have likely noticed a frustrating trend: everything is in Python, or it focuses on simplistic "Hello World" scripts that immediately crumble in a real-world enterprise environment. Welcome to the definitive guide on building production-ready agentic AI systems in the .NET ecosystem. Moving beyond theory, this course focuses on the hands-on development of autonomous multi-agent orchestration for enterprise applications. Powered by the Microsoft Agent Framework, Microsoft Foundry, the Model Context Protocol (MCP), Aspire, AG-UI, DevUI and .NET, you will learn how to build robust AI workflows that solve complex business problems. This course is designed to give you production-grade visibility and control from day one, integrating enterprise-grade AI agent patterns for real-world business workflows. What You Will Master In this comprehensive enterprise course, we move beyond basic prompt engineering into deep architectural implementation natively in C#: • Microsoft Agent Framework (MAF): Deep dive into Microsoft's framework for building sophisticated, stateful AI systems, utilizing Microsoft Foundry and Azure OpenAI as your cognitive engines. • Multi-Agent Orchestration: Design and implement complex workflow patterns (Sequential pipelines, Concurrent execution, dynamic Handoffs, and Group Chats) using Swarm Intelligence and the WorkflowBuilder. • Agentic RAG Systems: Rethink traditional, rigid RAG pipelines. Build intelligent, intent-based retrieval systems using Qdrant vector databases and the TextSearchProvider, allowing the AI to autonomously decide when and how to search your enterprise knowledge. • Protocols & Interoperability: Master the bleeding edge of integration: Agent-to-Agent (A2A) network communications, the Model Context Protocol (MCP) for tool exposure, and the AG-UI protocol for generative frontend streaming. • Observability & Visual Testing: Achieve production-grade visibility using .NET Aspire and DevUI. Visually track JSON payloads, token usage, agent handoff latencies, and tool calls in real-time. • Enterprise Microservices Integration: Scaffold a complete MinimalAgent microservices architecture, learning exactly how to integrate AI agents seamlessly into existing backends and web APIs. Course Roadmap and Structure This curriculum is structured across 4 comprehensive parts, designed to systematically take you from foundational agent anatomy to advanced multi-agent enterprise integration: Part 1: Core Agent Development We start by mastering the foundational anatomy of an AI Agent. You will establish your connections to the Azure OpenAI service and explore the agent invocation lifecycle. You will get hands-on by developing custom function tools—utilizing the AIFunctionFactory to automatically generate JSON schemas from native C# methods. Finally, we will implement the AgentSession class to give your agents the persistent context and memory required for meaningful enterprise interactions. Part 2: Orchestrating Multi-Agent Systems Once we understand the individual agent, we scale up to Swarm Intelligence. You will learn to design collaborative networks of highly specialized micro-agents (e.g., triage, finance, compliance). We will use the AgentWorkflowBuilder to architect distinct industry-standard topologies: Sequential, Concurrent, Group Chat, and Handoff patterns. We will integrate these swarms into our MinimalAgent .NET Aspire architecture, utilizing DevUI to visually test and watch real-time debates and handoffs unfold graphically. Part 3: Advanced Reasoning: Agentic RAG Move beyond traditional RAG. In this module, we build Agentic RAG—where the AI itself decides if it needs to query the database. To support enterprise scale, we will integrate .NET with Qdrant Vector Stores. You will learn how to generate embeddings, execute semantic searches, and tie it all together using the TextSearchProvider, turning your corporate data into a cognitive tool your agents can wield autonomously. Part 4: Agent Communications and Protocols Solve the enterprise interoperability challenge. We will expose your MAF agents as network-accessible Web APIs to establish secure A2A (Agent-to-Agent) architectures. We will dive deep into the Model Context Protocol (MCP), designing architectures that connect your C# agents to both local and hosted MCP servers—giving them autonomous access to external repositories and documentation. Finally, we will leverage the AG-UI Protocol to push interactive, generative UI components directly to the client frontend. Technology Stack • Languages & Frameworks: .NET 10, C#, ASP.NET Core, Blazor Server • AI & Agents: Microsoft Agent Framework, Azure OpenAI (gpt-5-mini, text-embedding-3-small) • Cloud & Deployment: Microsoft AI Foundry • Frontend Protocol: AG-UI — Agentic UI streaming protocol • Orchestration: .NET Aspire for Service Discovery and Container Lifecycle Management • Observability: Aspire OpenTelemetry, Application Insights • Vector Data & Storage: Qdrant Vector Databases • Architecture: Microservices, Clean Architecture, Model Context Protocol (MCP)
The Complete Full Stack AI Engineering Bootcamp
Udemy · 19.99 USD · ⭐ 4.46 · ⏱ 56,5 h · Inglés
Welcome to the step by step roadmap to becoming an AI engineer This course is designed to take you from the very basics to advanced real world AI engineering with a clear learning path. You will not just learn concepts. You will build systems, understand why they work, and learn how different pieces of AI, data, machine learning, deep learning, and LLM engineering connect together in real projects. Most courses teach tools in isolation. This course teaches you how to connect everything into one complete AI engineering workflow that is used in industry. In this course you will start from the foundations and gradually move towards advanced AI systems used today. You will learn • Python programming from beginner to advanced concepts for AI work • How data is represented using vectors and matrices • Data visualization, statistics, and feature engineering techniques • SQL and PostgreSQL which every AI engineer must know • Supervised machine learning algorithms like regression, SVM, decision tree, and XGBoost using scikit learn • Unsupervised learning algorithms like K means and DBSCAN with proper visualization • How to build APIs using FastAPI and run AI applications inside Docker containers • Data engineering fundamentals with Kafka and Spark architecture and why data engineering is critical in AI projects • Build scalable AI engineering pipelines using Azure Databricks, PySpark, Delta Lake, and end to end data workflows • Deep learning foundations and the mathematics behind neural networks • CNN and how machines process and understand images • NLP fundamentals like tokenization, embeddings, and word embeddings • Sequence models including RNN, LSTM, and GRU • PyTorch from scratch and building ANN, CNN, and RNN models using PyTorch • Transformer architecture explained step by step in simple terms • LLM engineering, RAG, advanced RAG architecture, LangChain, LangGraph, AI agents, and LLM workflows • Master MLflow for AI engineering, including experiment tracking and prompt experimentation and evaluation • MCP and how to build a local MCP server and client • Practical Guide to Running Open Source LLMs Locally using Ollama This is a complete theory plus hands on course. You will write code, build projects, deploy models, and understand the logic behind every step instead of copying and pasting code. If you want to transition your career into AI, understand how modern AI systems are built, and become job ready as an AI engineer, this course is for you. Join this course and start your journey to become a full stack AI engineer.
AI Agents & Workflows - The Practical Guide
Udemy · 19.99 USD · ⭐ 4.45 · ⏱ 6,5 h · Inglés
COURSE UPDATE This course was updated to reflect the change in what agents are today, more relevant capabilities, and emerging frameworks for building such agents. The course is now less about code deep dives, more about the core concepts (and how to apply them when using or building agents). --- "AI Agents" is a buzzword! That's a pity — because real AI agents (and AI-powered workflows) can unlock a huge range of opportunities. From summarizing documents and generating files, to customer support assistants, research helpers, and automations you wire into tools like Slack. Your imagination is the limit. This course cuts through the hype and shows you what AI agents actually are - how they work behind the scenes, how they differ from AI workflows, and how you can use and build both confidently. You'll look at agents from two angles: as a user of tools like Claude Cowork / ChatGPT-style agents, and as a builder who wants to plan or create agents visually or in code. The goal is a deep, practical mental model — not empty buzzwords. With this course, you'll get: • A clear explanation of what AI agents are (and what they are not) • The difference between AI workflows and AI agents — and when to choose which • The core building blocks: models, harnesses, the agent loop, tools, instructions, context/sessions, skills, memory, sandboxes, and humans-in-the-loop • How general agents differ from task-specific agents, and where agents can run (your machine vs the cloud) • Concrete examples you can follow: visual builds with n8n, code examples in Python, plus framework-based builds • Guidance that transfers beyond one stack — so you can apply the ideas with the models and tools you already use You'll learn: • What an agent harness is and how the agent loop really works • How LLMs use tools (and what models can and cannot do on their own) • How to steer agent behavior with system instructions, AGENTS .md / CLAUDE .md, and agent skills (progressive disclosure) • How to manage context, sessions, compaction, and longer-term memory concepts • How to build an AI workflow visually (e.g. a document summarizer in n8n) and the same idea in code • How to build AI agents visually (tools like summarize text & create PDF) and step-by-step in code • How “primitive” tool calling works vs provider-native tool calling • How more general agents support files, commands, project instructions, and skills • How to constrain agents with sandboxes and human approval (e.g. customer-support style refunds) • How frameworks speed this up — including a deep dive into Eve, a look at CrewAI, and an overview of other options (e.g. Vercel AI SDK, LangGraph, Pydantic AI) • How a task-specific agent can be exposed beyond your laptop (including a Slack demo) This is not primarily a programming course! You don’t need to be a developer to follow along: the code and visual projects are there so you can see how agents work. If you do build software, the same foundations apply whether you use Python, TypeScript, n8n, or a framework. By the end, you won’t just recognize the buzzword “AI agent” - you’ll understand the moving parts, know when a simple workflow is the better choice, and be ready to work with agents productively or design your own.
Big Data Engineering Bootcamp with GCP, and Azure Cloud
Udemy · 19.99 USD · ⭐ 4.45 · ⏱ 74 h · Inglés
Course Description In today’s data-driven world, organizations are dealing with massive amounts of data generated every second. Big Data technologies have become essential for efficiently processing, storing, and analyzing this data to drive business insights. Whether you are a beginner, fresher, or an experienced professional looking to transition into Big Data Engineering, this course is designed to take you from zero to expert level with real-world, end-to-end projects. This comprehensive Big Data Bootcamp will help you master the most in-demand technologies like Hadoop, Apache Spark, Kafka, Flink, and cloud platforms like AWS, Azure, and GCP. You will learn how to build scalable data pipelines, perform batch and real-time data processing, and work with distributed computing frameworks. We will start from the basics, explaining the fundamental concepts of Big Data and its ecosystem, and gradually move toward advanced topics, ensuring you gain practical experience through hands-on projects. What You Will Learn? • Big Data Foundations – Understand the 3Vs (Volume, Velocity, Variety) and how Big Data technologies solve real-world problems. • Data Engineering & Pipelines – Learn how to design ETL workflows, ingest data from multiple sources, transform it, and store it efficiently. • Big Data Processing – Gain expertise in batch processing with Apache Spark and real-time streaming with Kafka and Flink. • Cloud-Based Big Data Solutions – Deploy and manage Big Data solutions on Azure, and GCP using services • End-to-End Projects – Work on industry-relevant projects, implementing scalable architectures, data pipelines, and analytics. • Performance Optimization – Understand best practices for optimizing Big Data workflows for efficiency and scalability. Who is This Course For? • Beginners & Freshers – No prior experience needed. Start your journey in Big Data Engineering from scratch. • Software Developers – Expand your skills into Big Data technologies like Hadoop, Spark, and Kafka. • Data Analysts & Scientists – Work with large datasets, ETL pipelines, and real-time processing. • Cloud & DevOps Engineers – Learn how to deploy and manage Big Data applications in cloud environments. • IT Professionals – Transition into Big Data Engineering with hands-on experience and industry-relevant projects. Prerequisites • Basic Computer Knowledge – No prior Big Data experience required. • Python or SQL (Optional) – Helps but is not mandatory. • Laptop with 8GB RAM & Internet Access – To run Big Data tools locally or on the cloud. By the end of this course, you will be job-ready, equipped with practical skills, and confident in working with Big Data technologies used by top companies worldwide. Enroll now and take your career to the next level with Big Data.
Complete Generative AI Course: RAG, AI Agents & Deployment
Udemy · 19.99 USD · ⭐ 4.45 · ⏱ 23 h · Inglés
This complete Generative AI course takes you from beginner to advanced with hands-on projects, real-world applications, and career-ready skills. You’ll learn the foundations of Generative AI, explore Large Language Models (LLMs), master frameworks like LangChain, LlamaIndex, CrewAI, and PydanticAI, and deploy your own AI solutions on the cloud. The course is tailored to equip you with both the knowledge and practical experience required to step into a Generative AI Engineer role. Each section includes quizzes & coding exercises to help you test your knowledge and reinforce your skills. What you’ll learn in each section • 1. Introduction – Get started with the course, understand what you will learn & set up Python environments (Colab, Jupyter, PyCharm). • 2. Generative AI – Foundation – Understand AI vs ML vs DL vs GenAI, dive into Large Language Models, and learn the Transformer architecture. • 3. Accessing LLMs in Python – Use OpenAI, Gemini, Groq, and Ollama LLMs, and connect them through LangChain and LlamaIndex. • 4. Prompt Engineering – Explore prompt templates, zero-shot, and few-shot prompting to effectively interact with LLMs. • 5. Building GenAI Chatbots – Build and deploy chatbots step by step using LangChain, LlamaIndex, Streamlit UI, and Streamlit Cloud. • 6. Retrieval-Augmented Generation (RAG) – Understand RAG, build RAG pipelines with LangChain and LlamaIndex, and create a PDF Q&A bot. • 7. AI Agents – Learn what AI agents are and build agents with PydanticAI, AutoGen, and CrewAI for multi-agent workflows. • 8. LLM Deployment – Deploy open-source LLMs with Ollama, Docker, and vLLM, and set them up on AWS EC2 for real-world usage. • 9. Model Context Protocol (MCP) – Understand MCP, build an MCP server, and integrate MCP tools with PydanticAI and CrewAI agents. • 10. Capstone Projects – Apply everything learned to build real-world AI projects: Enterprise Chatbots, RAG Assistants, and Intelligent AI Agents with Full Cloud Deployment.
Vibe Coding desde Cero: Aprende a "Programar" Usando IA
Udemy · 19.99 USD · ⭐ 4.45 · ⏱ 2 h · Español
¿Y si pudieras crear una aplicación completa sin escribir ni una sola línea de código a mano? ¿Y si bastara con saber lo que querés construir, y explicárselo bien a una inteligencia artificial? Bienvenido al universo del Vibe Coding, una nueva forma de programar… sin programar. Este curso está diseñado para enseñarte cómo trabajar codo a codo con la inteligencia artificial para crear software real: aplicaciones, juegos, interfaces, automatizaciones y mucho más, sin necesidad de conocimientos avanzados en programación. Aquí no vas a memorizar sintaxis. Vas a aprender a pensar, estructurar y comunicar tus ideas de forma clara, guiando a la IA para que construya contigo. ¿QUÉ VAS A APRENDER? • Qué es el Vibe Coding, de dónde surge y por qué es una revolución en marcha • Cómo generar aplicaciones completas con herramientas como Cursor, Windsurf, Bolt y v0 • Cómo crear interfaces visuales, y apps funcionaleS desde un solo prompt • Técnicas y estrategias para redactar prompts claros, eficientes y poderosos • Cómo trabajar con iteraciones, reglas, estructura de archivos y estilo visual • Casos prácticos con proyectos reales: juegos, landing pages, apps administrativas y más • Buenas prácticas para mantener tu código legible, comprensible y sostenible en el tiempo • Reflexiones éticas y profesionales sobre el uso de IA en programación ¿CÓMO ESTÁ ORGANIZADO ESTE CURSO? Este curso está dividido en 4 grandes secciones: • Fundamentos del Vibe Coding: Entiende de qué se trata esta nueva metodología, cómo surgió y por qué está transformando la industria. • Herramientas esenciales: Aprende a usar Cursor, Windsurf, Bolt y v0: desde la instalación hasta el primer proyecto real. • Proyectos prácticos: Aplicá todo lo aprendido en 4 proyectos completos, uno con cada herramienta. Desarrollarás desde una app de notas hasta un juego de memoria visual y una landing para eventos. • Desafíos y reflexiones finales: Cómo mejorar tus pedidos, cómo cuidar la calidad del código, y qué impacto tiene este enfoque en el mundo profesional y ético del desarrollo. POR QUÉ PUEDO ENSEÑARTE Mi nombre es Federico Garay, soy VIP Instructor Partner en Udemy, y la mayoría de mis cursos son Best Sellers a nivel mundial. Cientos de miles de personas aprendieron a programar con mis 50 cursos, tales como Python Total, Python para Data Science, SQL Total, JavaScript Total, y tantos más. Este curso está pensado para que puedas construir sin escribir código manualmente, usando lenguaje natural, lógica clara y pensamiento creativo. MENSAJE FINAL El Vibe Coding no es solo una técnica. Es una forma de crear sin miedo, sin barreras técnicas y sin límites mentales. Si alguna vez tuviste una idea y pensaste: “no puedo hacerla porque no sé programar”,este curso es para vos. ¡Nos vemos en la lección #1! FEDE
AI Mastery Bootcamp 2026: Complete Guide with 1000 Projects
Udemy · 109.99 USD · ⭐ 4.45 · ⏱ 58,5 h · Inglés
Artificial Intelligence is no longer optional. With tools like ChatGPT, Claude, DeepSeek AI, Mistral, Google Gemini, Microsoft Copilot, Perplexity AI, Meta AI, GitHub Copilot, Jasper AI, Writesonic, YouChat, Chatsonic, and Hugging Face models transforming the way we work, learn, build, and automate, everyone needs to understand how AI works and how to use it effectively. Welcome to AI Mastery Bootcamp 2026: Complete Guide with 1000 Projects — a complete, hands-on Artificial Intelligence training program designed to help you go from beginner to confident AI builder. This bootcamp is your practical guide to mastering modern AI, from the foundations of Python and machine learning to today’s most in-demand AI technologies, including Generative AI, Large Language Models, AI Agents, Agentic AI Workflows, Retrieval-Augmented Generation, Multimodal AI, Deep Learning, NLP, Computer Vision, Prompt Engineering, LLM Applications, and AI Automation. Unlike traditional AI courses that focus only on theory, this bootcamp is built around 1000 real-world AI projects so you can learn by building. You will create practical AI systems, experiment with modern tools, and develop the confidence to apply AI to business, productivity, software development, content creation, automation, data analysis, and real-world problem solving. Throughout this course, you will learn the fundamentals of Artificial Intelligence, including Python programming, data preprocessing, machine learning, model training, model evaluation, and AI problem framing. You will then move into advanced topics such as neural networks, deep learning, natural language processing, computer vision, transformers, embeddings, vector databases, RAG systems, AI assistants, autonomous agents, and multi-agent systems. You will also explore popular AI frameworks and platforms used by modern developers and AI teams, including TensorFlow, PyTorch, Hugging Face, LangChain, LangGraph, CrewAI, AutoGen, OpenAI APIs, Claude APIs, Gemini, Mistral, DeepSeek, Ollama, FastAPI, Streamlit, and AI coding assistants. As AI evolves beyond simple chatbots, this course also introduces you to the next generation of AI systems: agentic AI systems that can plan, reason, use tools, retrieve information, automate workflows, and assist with complex tasks. You will learn how AI agents work, how RAG improves AI accuracy, how multimodal AI uses text, images, audio, and documents, and how production-ready AI systems are designed, deployed, monitored, and improved. This course is beginner-friendly, but it is also practical enough for learners who want to build a serious AI portfolio. Whether you want to become an AI Engineer, AI Developer, AI Product Builder, AI Automation Specialist, Prompt Engineer, Data Scientist, Machine Learning Engineer, or AI Entrepreneur, this bootcamp gives you a complete roadmap. By the end of AI Mastery Bootcamp 2026, you will have built a large portfolio of AI projects and gained practical experience with the tools, workflows, and technologies shaping the future of AI. What You Will Learn You will learn how to: Create AI projects using Python, machine learning, and deep learning Build real-world AI applications using modern AI tools and APIs Understand Generative AI, LLMs, transformers, embeddings, and prompt engineering Build AI assistants, chatbots, RAG systems, and LLM-powered applications Create AI agents that can plan, reason, use tools, and complete tasks Work with frameworks like TensorFlow, PyTorch, Hugging Face, LangChain, LangGraph, CrewAI, and AutoGen Use AI coding assistants like GitHub Copilot, ChatGPT, Claude, and Gemini to accelerate development Build NLP, computer vision, data science, automation, and multimodal AI projects Deploy AI applications using FastAPI, Streamlit, cloud platforms, and APIs Understand Responsible AI, AI safety, governance, bias, privacy, and ethical AI design Develop a strong AI project portfolio with 1000 practical projects Key Highlights of the Course Complete AI Bootcamp updated for 2026 1000 hands-on AI projects Beginner-friendly step-by-step learning path Covers Python, machine learning, deep learning, NLP, and computer vision Includes Generative AI, LLMs, RAG, AI Agents, and Agentic AI Workflows Covers modern tools like ChatGPT, Claude, Gemini, DeepSeek, Mistral, Hugging Face, and GitHub Copilot Learn frameworks such as TensorFlow, PyTorch, LangChain, LangGraph, CrewAI, and AutoGen Build portfolio-ready projects for jobs, freelancing, startups, and business use cases Includes AI automation, productivity workflows, and real-world business applications Covers Responsible AI, AI governance, safety, and ethical AI practices Who This Course Is For This course is designed for: Beginners who want to learn Artificial Intelligence from scratch Students who want to build a strong AI project portfolio Software developers who want to move into AI engineering Data analysts and data scientists who want to expand into modern AI Entrepreneurs who want to build AI-powered products Business professionals who want to understand AI tools and automation Product managers and leaders who want to understand AI systems Anyone who wants to learn ChatGPT, Claude, Gemini, AI agents, RAG, and modern AI development Why Take This AI Bootcamp? Artificial Intelligence is changing every industry. Companies are adopting AI for automation, customer support, software development, marketing, analytics, cybersecurity, operations, research, and decision-making. The people who understand how to build and apply AI will have a major advantage. This course does not just teach you AI concepts. It helps you build real projects, understand real tools, and develop practical skills that you can use immediately. You will not only learn what AI is — you will learn how to create with AI. Enroll Today Join AI Mastery Bootcamp 2026: Complete Guide with 1000 Projects and start building the AI skills needed for the future. Learn Artificial Intelligence. Build real projects. Master modern AI tools. Create your AI portfolio. Enroll today and become part of the AI revolution.
Curso de R y Python para Data Science y Análisis de Datos
Udemy · 59.99 USD · ⭐ 4.45 · ⏱ 17,5 h · Español
Este curso es para aprender R y Python desde cero mientras programas. Es un curso completamente práctico en el que tendrás la base necesaria para dominar y realizar análisis más avanzados con ambos programas. Comenzaremos con la instalación de todo lo necesario para programar en nuestro ordenador y te enseñaremos también las herramientas de la nube que tienes disponible de forma gratuita para que ni siquiera tengas que instalar nada. Luego veremos los fundamentos principales de cada lenguaje, cómo se configura, cómo se trabaja, cómo se definen las cosas, cómo se crean funciones y códigos, etc. Posteriormente, veremos los diferentes paquetes y librerías más usadas (Pandas, Seaborn, ggplot2, etc.) de cada lenguaje, que podemos utilizar en nuestros análisis de datos. Y paso a paso veremos las diferentes formas de trabajar con los datos: cómo importarlos, cómo guardarlos, cómo descargar datos de internet y usarlos para analizarlos, cuáles son las principales partes del pre-procesado de datos, el análisis estadístico descriptivo, los diferentes gráficos que podemos crear en base a nuestros datos, y cómo podemos personalizar todas esas visualizaciones. Todo ello utlizando múltiples casos prácticos reales. Y a su vez, tendrás a tu disposición muchísimo material complementario gratuito así como todos los códigos y scripts tanto de R como de Python, para que todo ese conocimiento lo puedas transferir rápidamente a tu propio campo y tus propios análisis. Así que si tomas este curso estarás preparado para manejar R y Python con total soltura, y podrás comenzar a estudiar métodos un poco más avanzados del análisis de datos programados en R o Python. ¡Un saludo y espero que nos veamos en clase! Elisa
Ultimate Guide to FastAPI and Backend Development
Udemy · 19.99 USD · ⭐ 4.45 · ⏱ 23 h · Inglés
Unlock the Power of APIs with Python and FastAPI! Ready to transform your Python skills into building blazing-fast, production-ready APIs? This hands-on course is your definitive guide to crafting high-performance APIs using FastAPI and Python. Go beyond theory and build a complete backend API for a real-world Delivery Management System. You'll master essential backend concepts like the request/response cycle, data modeling, databases, asynchronous programming, and dependency injection – all while leveraging the power of FastAPI's built-in features: asynchronous speed, automatic data validation, OpenAPI support, and exceptional developer productivity. Python Even though having a basic understanding of python is required, core language concepts like type hinting, decorator functions, context managers, async programming and others will still be covered. SQL Database We will cover databases and SQL from the basics. Especially if you're a beginner, we will first learn the need for structured databases, what SQL is, how to define tables and write SQL queries. Then we will integrate a SQLite database manually with our FastAPI application and then leverage modern tools like SQLModel to define database table schema and FastAPI's powerful dependency injection to provide database session to API endpoints. After covering the essentials, we will move onto advanced concepts like asynchronous database (PostgreSQL), manage schema changes with alembic migrations, define one-to-many and many-to-many SQL relationships and link models. User Authentication FastAPI has built-in support for OAuth2 Scheme. Implement secure user authentication with OAuth2 Password Flow and handle user signups, email verification of registered users, login, control access to endpoints with JWT, allow password resets and logout users. Email and SMS Notifications In our delivery management system, we will send email updates and sms to recipients on shipment updates. Like sending tracking links with email on order placed, shipment status updates, and sending verification codes or OTP as sms to recieve shipments. API Testing Learn the basics of the pytest - testing framework. Write and run simple tests, and use fixtures to manage test state. Then, write test for API endpoints with FastAPI's built-in test client. We will cover required concepts when testing APIs like setting up test databases, overriding dependencies, authentication in test functions and automate testing with schemathesis. Frontend? As a bonus, we will also cover frontend web development with React JS. Covering the essentials like components, state management, hooks, context providers and routing. Using modern and meme proof tools like Vite, Axios, React Router, Shadcn UI, and Tanstack React Query. Docker Learn the industry standard tool for packaging applications. We will cover the basics of docker and the essential concepts of containerisation. And run our API Server, PostgreSQL database, Redis and Celery background worker seamlessly with Docker Compose. If you're a Python developer looking to create fast, reliable, and modern web APIs, and if you're eager to understand and build the backend yourself, then this course is your perfect starting point. Join us in the first lesson and start building today!
Claude AI Masterclass: Claude Code, MCP, Agents & Automation
Udemy · 49.99 USD · ⭐ 4.45 · ⏱ 13 h · Inglés
Master Claude AI End-to-End — From Prompt Engineering to AI Agents, MCP, Automation, APIs and Claude Code Claude AI is one of the most powerful AI platforms available today, leading the industry in coding, reasoning, writing, and agentic workflows. But most people only use Claude as a chatbot. This course is designed to take you far beyond simple prompting and show you how to build real-world AI systems, autonomous agents, production automations, and developer workflows using the complete Claude ecosystem. Welcome to Claude AI Masterclass: Agents, MCP & Claude Code — a comprehensive, hands-on, project-driven course that takes you from beginner to advanced Claude practitioner. Whether you want to build AI Agents, automate repetitive work, integrate Claude into applications, develop with Claude Code, create MCP servers, or master advanced prompting techniques, this course gives you everything you need in one place. Why This Course Is Different Most Claude courses teach prompting. This course teaches implementation. You'll learn how to use Claude across: • Claude Code • AI Agents • Model Context Protocol (MCP) • Anthropic API • Desktop Automation • Browser Automation • Excel and PowerPoint Workflows • Plugins, Skills and Hooks • Production AI Applications Every section is practical, hands-on, and focused on real workflows you can immediately apply to your career, business, or projects. No fluff. No theory overload. Just practical Claude skills that matter. What You Will Master Claude Code: AI-Powered Software Development Master Claude Code across Terminal, VS Code, Desktop, and Web environments. Learn how to: • Generate production-ready code • Debug applications and resolve complex errors • Refactor legacy codebases • Write automated tests • Review pull requests • Automate Git workflows • Perform security reviews • Use project instruction files • Create custom Skills • Build Plugins • Implement powerful Hooks You'll discover how modern developers are using Claude Code to dramatically accelerate software delivery. Building AI Agents with Claude Learn how to build autonomous AI systems using Claude. Topics include: • Claude Agent SDK • Production Agent Development • Multi-Agent Systems • Agent Orchestration • Agents Calling Agents • Tool-Using Agents • Google ADK with Claude Integration By the end, you'll understand how to build real-world agentic workflows instead of simple chatbot experiences. Model Context Protocol (MCP) MCP is rapidly becoming one of the most important standards in AI. You'll learn: • MCP fundamentals • Connecting Claude to external tools • Setting up MCP servers in Claude Desktop • Running MCP servers in Claude Code • Filesystem MCP servers • GitHub MCP integrations • Search MCP integrations • Creating custom MCP servers from scratch You'll gain practical experience connecting Claude to real-world systems and data sources. Claude CoWork: Desktop Automation for Everyone Turn Claude into your digital employee. Learn how to automate: • File management • Document processing • PDF intelligence workflows • Data extraction pipelines • Gmail workflows • Google Drive automation • Slack integrations • Scheduled tasks • Recurring workflows • Plugin customization • Custom Skills You'll build automation systems that save hours of manual work every week. Anthropic API: Integrating Claude into Applications Go from your first API call to production-ready AI applications. You'll learn: • Anthropic Python SDK • API Authentication • Streaming Responses • Vision Capabilities • PDF Processing • CSV Processing • Structured Outputs • Pydantic Validation • Prompt Caching • Batch Processing • Token Optimization • Model Selection • Extended Thinking Everything is taught using practical examples and real code. Claude in Browser, Excel and PowerPoint Extend Claude into the tools you already use every day. Learn how to: • Use Claude in Chrome • Run large-scale web research • Automate browsing tasks • Analyze spreadsheets • Generate formulas • Create dashboards • Generate presentations • Transform data into slides Claude Chat Mastery Master advanced prompt engineering techniques including: • Zero-Shot Prompting • Few-Shot Prompting • Chain-of-Thought Prompting • System Prompts • XML Prompting • Delimiters and Structured Inputs • Prompt Chaining • Content Creation Workflows • Business Analysis Workflows Learn how professionals consistently get better results from Claude. Solving Context Window Limits One of the biggest challenges with any AI model is managing large projects and long conversations. You'll learn proven techniques including: • Summarize and Continue • Memory Injection • Context Briefs • XML Conversation History • Chunked Task Delegation • Claude Memory These techniques allow you to work effectively on large-scale projects without losing context. Real Projects You Will Build Throughout the course you'll create practical projects such as: • AI Agents • Multi-Agent Systems • Custom MCP Servers • Claude API Applications • Browser Automation Workflows • Document Intelligence Pipelines • Email Automation Systems • Custom Skills and Plugins • Git Automation Workflows • Security Review Pipelines • Software Development Workflows with Claude Code Every lesson focuses on implementation rather than theory. By The End Of This Course You Will Be Able To • Build AI Agents with Claude • Create Multi-Agent Workflows • Develop Custom MCP Servers • Connect Claude to External Tools • Integrate Claude into Applications via the Anthropic API • Automate Repetitive Business Processes • Use Claude Code to Develop Software Faster • Build Custom Skills, Plugins and Hooks • Solve Context Window Challenges • Create Production-Ready AI Systems • Use Claude Across Chrome, Excel, PowerPoint and Desktop Workflows • Design End-to-End Automation Solutions What Makes This Course Stand Out • Complete Claude Ecosystem Coverage — Claude Chat, Claude Code, AI Agents, MCP, Anthropic API, Claude CoWork, Chrome, Excel, and PowerPoint • Hands-On Learning — Build real projects instead of watching theory-heavy lectures • Beginner-to-Advanced Path — Start with fundamentals and progress to advanced implementations • Real Business Automation — Learn workflows that save hours of work every week • Developer-Focused Content — Build production-ready AI applications and integrations • Covers MCP, Skills, Plugins and Hooks — Topics missing from most Claude courses • Regular Course Updates — Stay current as Claude continues to evolve • Lifetime Access and 30-Day Money-Back Guarantee Enroll Today AI is no longer just about asking better questions. The next generation of AI is about building agents, automations, integrations, and intelligent workflows that can perform meaningful work on your behalf. Claude is leading that shift. If you want to move beyond basic prompting and learn how to build real AI agents, MCP integrations, automation systems, and production-ready applications, this course provides the complete roadmap. Whether you're automating your daily work, accelerating software development with Claude Code, integrating Claude into applications through APIs, or building advanced agentic systems, you'll gain practical skills you can apply immediately. Enroll now and start mastering the complete Claude ecosystem.
pyRevit for Revit: Tools, Scripts & BIM Automation
Udemy · 19.99 USD · ⭐ 4.45 · ⏱ 6,5 h · Inglés
Note: PyRevit is one of the best Revit plugins that helps you speed up your workflow, create your own custom plugin in Revit to enhance efficiency, and optimize the 3D modeling and BIM process. 1. How can BIM specialists fully automate modeling processes in Revit and enhance productivity? 2. What are the differences and advantages between PyRevit and Dynamo that this course specifically focuses on? 3. How can we leverage the power of PyRevit to optimize BIM projects even without programming knowledge? The comprehensive PyRevit course in Revit is a complete training program designed to increase speed and productivity in BIM projects. by using this powerful, Open source plugin, you can automate repetitive processes, improve modeling accuracy, and optimize BIM workflows. This course helps engineers and BIM specialists elevate their performance in Revit to a professional level using Python and PyRevit tools, allowing them to complete complex projects faster and with greater precision. Some of the topics you will learn in this course: • Automate tasks in Revit to boost productivity. • Speed up modeling without losing accuracy. • Optimize BIM workflows for faster project delivery. • Create custom Python scripts for automation. • Manage views, sheets, and tags efficiently. • Detect and fix errors in modeling. • Use pre-built PyRevit tools for time-saving. • Master a free, open-source plugin for cost-effective solutions. Boost your speed and accuracy in Revit with the comprehensive PyRevit course and make modeling smarter! If you're tired of repetitive tasks, this course teaches you how to automate your workflow, increase productivity, and Optimize BIM processes using Python and PyRevit. No programming knowledge is required complete complex tasks faster with just a few clicks and take advantage of a free, open-source plugin to reduce costs. Enroll in this course right now, and feel free to ask me any questions throughout your learning journey so you can implement this process in the best possible way. I’m with you every step of the way to help you become a professional.
Full stack generative and Agentic AI with python
Udemy · 19.99 USD · ⭐ 4.44 · ⏱ 32,5 h · Inglés
Welcome to the Complete AI & LLM Engineering Bootcamp – your one-stop course to learn Python, Git, Docker, Pydantic, LLMs, Agents, RAG, LangChain, LangGraph, and Multi-Modal AI from the ground up. This is not just another theory course. By the end, you will be able to code, deploy, and scale real-world AI applications that use the same techniques powering ChatGPT, Gemini, and Claude. What You’ll Learn Foundations • Python programming from scratch — syntax, data types, OOP, and advanced features. • Git & GitHub essentials — branching, merging, collaboration, and professional workflows. • Docker — containerization, images, volumes, and deploying applications like a pro. • Pydantic — type-safe, structured data handling for modern Python apps. AI Fundamentals • What are LLMs and how GPT works under the hood. • Tokenization, embeddings, attention, and transformers explained simply. • Understanding multi-head attention, positional encodings, and the "Attention is All You Need" paper. Prompt Engineering • Master prompting strategies: zero-shot, one-shot, few-shot, chain-of-thought, persona-based prompts. • Using Alpaca, ChatML, and LLaMA-2 formats. • Designing prompts for structured outputs with Pydantic. Running & Using LLMs • Setting up OpenAI & Gemini APIs with Python. • Running models locally with Ollama + Docker. • Using Hugging Face models and INSTRUCT-tuned models. • Connecting LLMs to FastAPI endpoints. Agents & RAG Systems • Build your first AI Agent from scratch. • CLI-based coding agents with Claude. • The complete RAG pipeline — indexing, retrieval, and answering. • LangChain: document loaders, splitters, retrievers, and vector stores. • Advanced RAG with Redis/Valkey Queues for async processing. • Scaling RAG with workers and FastAPI. LangGraph & Memory • Introduction to LangGraph — state, nodes, edges, and graph-based AI. • Adding checkpointing with MongoDB. • Memory systems: short-term, long-term, episodic, semantic memory. • Implementing memory layers with Mem0 and Vector DB. • Graph memory with Neo4j and Cypher queries. Conversational & Multi-Modal AI • Build voice-based conversational agents. • Integrate speech-to-text (STT) and text-to-speech (TTS). • Code your own AI voice assistant for coding (Cursor IDE clone). • Multi-modal LLMs: process images and text together. Model Context Protocol (MCP) • What is MCP and why it matters for AI apps. • MCP transports: STDIO and SSE. • Coding an MCP server with Python. Real-World Projects You’ll Build • Tokenizer from scratch. • Local Ollama + FastAPI AI app. • Python CLI-based coding assistant. • Document RAG pipeline with LangChain & Vector DB. • Queue-based scalable RAG system with Redis & FastAPI. • AI conversational voice agent (STT + GPT + TTS). • Graph memory agent with Neo4j. • MCP-powered AI server. Who Is This Course For? • Beginners who want a complete start-to-finish course on Python + AI. • Developers who want to build real-world AI apps using LLMs, RAG, and LangChain. • Data Engineers/Backend Developers looking to integrate AI into existing stacks. • Students & Professionals aiming to upskill in modern AI engineering. Why Take This Course? This course combines theory, coding, and deployment in one place. You’ll start from the basics of Python and Git, and by the end, you’ll be coding cutting-edge AI applications with LangChain, LangGraph, Ollama, Hugging Face, and more. Unlike other courses, this one doesn’t stop at “calling APIs.” You will go deeper into system design, queues, scaling, memory, and graph-powered AI agents — everything you need to stand out as an AI Engineer. By the end of this course, you won’t just understand AI—you’ll be able to build it.
Learn TradingView Pine Script Programming From Scratch
Udemy · 84.99 USD · ⭐ 4.44 · ⏱ 22 h · Inglés
TradingView is the go-to platform for traders worldwide, known for its clean, intuitive charts and access to hundreds of global exchanges across multiple asset classes. Whether you're a beginner or an expert, TradingView is the perfect tool to take your trading to the next level. But here's the catch: even with its amazing features, TradingView has its limitations—unless you know Pine Script. This course is designed to empower you with the skills to unlock the full potential of TradingView. You'll learn how to create custom indicators, automate your trading strategies, and set up alerts that work for you—without any limits. Pine Script is the key to customizing TradingView’s powerful charting library and building tools that fit your exact needs, giving you a serious edge over other traders who rely solely on default indicators. In this course, you’ll not only master Pine Script, but you'll also learn how to build dynamic, profitable strategies that can automate your trades and free up your time. Whether you want to streamline your analysis, create sophisticated signals, or automate your trading logic, this course will give you the skills and knowledge to make it happen. By the end, you'll be a Pine Script expert—ready to build and implement your own custom tools, strategies, and alerts to make smarter, more efficient trades every day. Don’t just use TradingView—master it and take your trading to new heights! Is This Course For Me? Yes, this course is for you if: • You want to become a better trader: Mastering Pine Script allows you to automate your trading strategies, refine your decision-making process, and adapt to ever-changing markets by tailoring custom indicators to your exact needs. • You want A to Z step-by-step Pine Script training: This course provides a structured progression from the fundamentals to advanced Pine Script concepts. You will gain both theoretical knowledge and practical coding skills in Pine Script V4, V5, and V6. • You want to program your own indicators and strategies: You’ll not only learn to create indicators but also to combine multiple indicators into powerful, integrated strategies. Learn how to program trend indicators, oscillators, and even custom alert systems to enhance your trading. • You want to turn your trading logic into an automated strategy: Through in-depth lessons, you will learn to translate your ideas into fully automated strategies. You’ll also understand how to fine-tune and optimize your trading bots for better performance and reduced risk. • You want to unlock TradingView’s full potential with code: This course teaches you how to go beyond TradingView’s default features, creating custom scripts that allow for advanced chart analysis, multi-timeframe strategies, real-time dashboards, and dynamic alerts that save you time and increase accuracy. • You want to learn how to program: This course is not just about Pine Script—it also introduces core programming principles. Whether you’re a beginner or already have coding experience, you’ll develop a strong programming foundation through hands-on practice and real-world projects. • You want to master advanced Pine Script techniques: Beyond the basics, you'll dive into concepts like optimization, debugging, multi-timeframe analysis, and code refactoring. Learn to build reusable components and integrate complex algorithms into your scripts. If you're ready to take your trading to the next level, create custom automated solutions, and gain solid programming expertise, this course is for you! What Skills Will I Learn in This Course? By the end of this course, you’ll gain the skills to: • Program in Pine Script V4, V5, and V6: Learn the latest Pine Script versions, ensuring you're always up-to-date with TradingView’s evolving capabilities. • Modify & Understand Existing Scripts & Build Your Own: You’ll confidently analyze, adapt, and enhance any Pine Script code to meet your specific needs. • Automate Trading Logic: Master the art of building fully automated trading strategies using Pine Script, saving you time and maximizing opportunities. • Monitor Multiple Assets on One Chart: View and track multiple assets simultaneously on a free TradingView account, simplifying your analysis and decision-making. • Combine Multiple Indicators into One: Streamline your trading by combining different indicators into custom, powerful tools that give you an edge. • Set Up Custom Alerts to Save Time: Free up screen space and time by setting up advanced, automated alerts based on your own unique trading conditions. • Write Better Code with Tips & Tricks: Discover expert tricks for writing more efficient code, bypassing some of TradingView’s Pro features, and improving your workflow. • Create Dynamic Data Visualizations: Learn how to visualize your trading data with custom charts, shapes, and labels, enhancing your analysis and decision-making. • Debug and Optimize Your Scripts: Troubleshoot and optimize your code for faster execution and fewer errors, ensuring your strategies perform at their best. • Master Multi-Timeframe Analysis: Incorporate multi-timeframe analysis into your strategies, giving you a deeper understanding of market trends and better signals. • Build Interactive Dashboards: Develop real-time dashboards displaying critical trading data like price action, market sentiment, and custom indicators. • Leverage Libraries for Reusability: Create and use libraries for reusable functions, streamlining your code and saving time in future projects. • Work with Advanced Mathematical Techniques: Apply sophisticated mathematical models, such as Fibonacci retracements, moving averages, and custom calculations, to elevate your trading strategies. • Use Conditional Logic for Smarter Strategies: Enhance your strategies with complex conditional logic that can handle a variety of market conditions. • Create and Customize Trading Indicators: Build your own custom indicators or modify existing ones to tailor your trading tools to your exact needs. • Handle Market Data Effectively: Learn how to process and manipulate market data like price, volume, and momentum for more informed trading decisions. This course doesn’t just teach you Pine Script—it empowers you with the knowledge to build custom trading tools, automate strategies, and unlock the full potential of TradingView, using a FREE TradingView account saving you hundreds of dollars in subscription fees!. Whether you're a beginner or looking to refine your existing skills, you’ll walk away with practical, actionable skills that you can start using right away. How Will I Learn? You will experience Pine Script through the eyes of a seasoned programmer and trader, drawing from years of continuous improvement and hands-on experience. Throughout the course: • Step-by-Step Guidance from Basic to Advanced Concepts: I will personally guide you through each lesson, ensuring you master everything from the fundamentals to advanced techniques at your own pace. • Multiple Approaches to Problem-Solving: I’ll show you various ways to achieve the same goal in Pine Script, helping you understand the pros and cons of each method so you can choose the best approach for your needs. • Hands-On Practice with Code-Alongs: Theory alone isn’t enough—we will be building real scripts together in the Pine Editor on TradingView. You'll apply what you’ve learned in real-time, strengthening your skills with every line of code you write. • Expert Support and Personalized Feedback: No question is too small or complex. As you progress, I’ll be there to answer all your questions and provide the guidance you need to keep moving forward confidently. Just be sure to join the free group for better response times. • Learn by Doing: By the end of the course, you’ll have not only a deep understanding of Pine Script but also practical experience creating custom indicators, strategies, and automating trading logic. With this course, you’ll not only learn to code—you’ll become proficient in using Pine Script to create powerful, customized trading tools that work for you! Is this course worth my time and money? Because this is a highly subjective question relating to personal experience, conditions, knowledge, mental characteristics, and states; the answer depends on the person taking this course a.k.a. YOU. With that in mind let me offer some objectives for your perceptions. • There is more than 21 hours of content in this course. You could theoretically learn Pine Script in a day and you don’t need to look anywhere else. I say in a day because 9 hours of the content is from version 4 • I teach you how to get past TradingView’s three indicator limit and show you techniques that allow you to monitor more than one asset on a chart. All on the free account. Yes, this course saves you the cost of a TradingView pro subscription. • I've spent almost 10 years learning what I know now. I've consolidated and condensed that knowledge into this course, so you can be confident you are learning the right things the right way. By doing this, I am saving you time so you don't have to do 10,000 google searches piecing together outdated blog posts, youtube tutorials, and difficult documentation. Put simply, you can learn all this stuff on your own and it will take you a while. Or you could take my course and I can teach you everything in 12 hours. • If you ever get stuck, I am available to answer questions and help you out! I have been working with Pine Script since 2015 and have extensive experience building indicators and backtesting strategies for myself and clients. I have worked with Python, Javascript, C++, C, and other Domain-Specific Languages like mql4 within the sphere of financial analysis and quantitative analysis. I’ve come across many frameworks for building and testing strategies, and can honestly say that Pine Script is now my go-to backtesting engine. It hasn’t always been that way, but Pine Script has been rapidly evolving and finally can do the things I need it to do rapidly. • I will continually improve and update the course as new features are added to Pine. • Finally, you can always get your money back if you're not satisfied! What will each section cover? This course is designed to take you from beginner to expert in Pine Script, starting with the basics and progressing through advanced topics. No prior programming experience is necessary! Here is an overview of each section with descriptions: • Pine Script v6 Overview - Get familiar with the new features and updates introduced in Pine Script v6. This section provides an overview of what's new and how to apply it in your scripts. • Introduction to Pine Script - Begin your Pine Script journey by setting up your first script. This section is ideal for beginners and will introduce you to the essentials. • Pine Script Primer - Learn how to navigate TradingView™ and use Pine Script effectively. We'll cover the basics of setting up and customizing your scripts in TradingView™ and introduce the Pine Editor. • Pine Script Syntax and Structure - Dive into the syntax and structure of Pine Script. You’ll explore the Pine Editor, the debugger, the execution process, and the differences between indicators, strategies, and libraries. • Working with Variables and Data Types - Master the core concept of variables in Pine Script, how to define and assign values, and how to use advanced concepts like user-defined types and variable persistence with the var keyword. • Operators and Expressions - Learn how to use arithmetic, comparison, and logical operators to control logic and build expressions in Pine Script. You'll apply these concepts by creating a multi-indicator signal generator. • Conditional Statements and Control Flow - Take your scripts to the next level with conditional statements like if, ternary operators, and switch statements. This section includes practical projects like building a mean reversion tool and a dynamic Bitcoin price indicator. • Visualizing Data - Learn to visualize your data on the charts using functions like plot(), plotarrow(), and plotshape(). You’ll also learn how to manipulate plot data and use advanced visualization techniques to enhance your trading signals. • Using Loops - Explore the power of loops in Pine Script. This section will teach you how to use for loops and while loops and apply them to practical scenarios like moving averages. • Functions - Understand how to create and use functions in Pine Script. You’ll learn how to return multiple values, handle local history, and implement best practices for efficient code execution. • Arrays - Learn how to create and manipulate arrays in Pine Script. This section also covers advanced techniques such as removing values and referencing historical data with arrays. • Labels - Master the creation and manipulation of labels in Pine Script. You’ll learn how to update and manage labels efficiently, using them in interactive data dashboards. • Lines - Gain expertise in creating and using lines in Pine Script. This section covers everything from basic line syntax to creating complex indicators like Fibonacci retracements and building a simple trading bot. • Tables - Learn how to work with tables in Pine Script. You’ll cover creating and modifying cells, merging tables, and using tables to track signals for better data analysis. • Maps - Explore the functionality of maps in Pine Script, including creating, reading, writing, and removing key-value pairs. Learn how to loop through nested maps and apply them in your projects. • Matrices - Master the manipulation of matrices in Pine Script. This section will teach you how to read and write matrix rows and columns, and how to loop through matrices for dynamic trading logic. • Methods - Unlock the power of overloaded methods in Pine Script. This section includes a project to build an advanced logger that uses methods to handle complex tasks. • Libraries - Learn how to create and work with libraries in Pine Script. This section will teach you how to export functions, use annotations, and publish your own libraries. You’ll convert an existing indicator into a reusable library. Previous Course Content (Version 4 with version 5 updates and migrations) • Quickstart - The purpose of this section is to get familiar with the Pine editor and build your first custom Pine Script! • Basic Syntax - We have to crawl before we can run. In this section, you will learn the fundamentals of Pine Script and test your understanding at the end. • Variables - These babies are the gears to our Pine Script engine. Getting a handle on variables will really put things into motion. You will learn about all the different types and forms of variables. You will learn how to store and retrieve data so you can use it in your custom scripts later. You will be quizzed at the end. • Variable Operators - This is the fun part. Building on the last section, after completing this section, you will know how to manipulate and control the logic in your code. This is the core of our algorithmic trading. You will be quizzed at the end. • Drawing On The Chart - There really is no use for Pine Script if you don’t know how to do this. By the end of this section, not only will you learn how to draw and plot everything to your charts, but you will also build a multi-chart with custom candles. You will combine indicators in a project and control your plots with inputs. You will plot better than Descartes himself. • Conditional Operations - Take everything we’ve learned so far and continue building on it. This section is all about conditional operations and how to make decisions in our code. By the end of this section, you will know how to program smart code and create decision trees based on pure logic. We will continue on our project from the last section. • Functions - The bread and butter of functional programming. Daunting at first, but don’t let this concept scare you away. You don’t need functions, but once you work with them, you will never want to work without them. By the end of this section, you will understand the concept of functional programming and you will be able to call yourself a real programmer if you can finish the final piece of the project. • Labels & Lines (New Course Addition, November 2020) - One of the newest and most powerful features of Pine Script Version 4. By the end of this section, you will be able to draw complex lines and labels. You will create an automatic Fibonacci tool, a trade analyzer, and a real-time dashboard with multiple indicator outputs. • Wrapping Up - A warm goodbye with special gifts. • Alerts (New Course Addition, March 2021) - We cover the new alert() function, teaching you ways to program multiple alert conditions in one script so you don't have to set up a bunch of different alerts with TradingView. We build a screener and cover dynamic messages in your alerts. Don't skip this section. • Version 5 Update (New Course Addition, October 2021) - Pine Script is moving fast! Version 5 comes with a lot of great improvements! Check out this section to see the major changes. Don't worry if you are still learning Version 4. Everything is mostly backward compatible; you will just need to learn a few namespace changes (5 minutes of your life) to upgrade and start using version 5 today! Projects: • Multi-Indicator Signal Generator – Built in Section 6, "Operators and Expressions." • Mean Reversion Trading Tool – Built in Section 7, "Control Flow with Conditional Statements." • Dynamic Bitcoin Price Indicator – Built in Section 7, "Control Flow with Conditional Statements." • Moving Averages Using Loops – Built in Section 9, "Loops." • Signal Generation Automation – Built in Section 10, "Functions." • Array Manipulation Project – Built in Section 11, "Arrays." • Real-Time Dashboard with Labels and Lines – Built in Section 12, "Labels." • Automatic Fibonacci Tool – Built in Section 13, "Lines." • Trade Analyzer – Built in Section 13, "Lines." • Advanced Logger Using Methods – Built in Section 17, "Methods." • Reusable Library for Logging – Built in Section 18, "Libraries." • Screener with Alerts – Covered in Bonus Content "Alerts (New in 2021)." • Dynamic Messages in Alerts – Covered in Bonus Content "Alerts (New in 2021)." What you get with the course: • 21.5 hours of on-demand video lessons • Assignments to reinforce learning • 8 articles for deeper understanding • 43 downloadable resources, including all the code I use in the course • 12 projects to practice and apply your knowledge • 30-day money-back guarantee • Instructor feedback – Yes, I care about my students and want you to be successful • Exclusive group access & extra resources for extended learning beyond course materials • Quizzes & activities to test your knowledge • Full lifetime access • Access on mobile and TV • Certificate of completion at the end of the course
The Complete Coding for Beginners Course (2026)
Udemy · 84.99 USD · ⭐ 4.44 · ⏱ 16 h · Inglés
Do you want to learn to code in 2026, and start a developer career? Or have you already started and feel lost because you don’t really understand what you’re coding? Welcome to the most comprehensive Coding for Beginners course - Zero experience needed! At the end of the course, you will become a developer. You will be able to create your own algorithms to solve any problem, and turn those algorithms into real code (Python, …). If you have to take any coding course first, this is the ONE. → Why this coding course? Coding can be quite hard when you get started. You have to learn so many things at the same time: coding logic, syntax, developer tools, frameworks, and much more. Because of this, it’s easy to get lost. And I realized that many people start their coding journey not really knowing what to do. Then the questions appear: Should you start with a language such as Python or Java? Should you go with web development or mobile apps? Which code editing software is the best? The truth is, those questions are not that important when you get started. What’s important at the beginning is to make progress on the programming fundamentals. With those fundamentals, everything else - for example programming languages - will be easier to learn. And you will be able to truly kick-start your developer career. It’s not just about the syntax of a language. Let’s say you learn the Python language. Well, you might learn the syntax, but then, how does this teach you how to code? Coding is not about syntax, coding is about solving real problems. That's why in this course, I focus on this problem-solving part. To give you the thinking tools you really need to become a great developer. Once you have this training, you can then expand on it to learn anything else. Now, how to learn those fundamentals the right way? That’s where things get complicated. When searching on the Internet, I didn’t find a lot of resources talking about this in a clear way, without focusing so much on one particular language. And that’s why I’ve created this course, which is the one I wish I had when I got started. → How will you learn to code? I’ve been a beginner myself, and I first learned the coding fundamentals. Today, I know this was the right way, although how it was taught in school was a bit boring. From this experience, and after many years coding and teaching coding, I’ve come up with a simplified approach to code that is going to give you not only the fundamentals you need, but that is also fun, step by step, and project-based. You will build real applications right from the start. In this course, I’m deconstructing programming by removing everything you don’t need at the beginning. I will teach you the very basics without any distraction. And then, little by little, we will add more things, until you reach a point when you can call yourself a developer. The method I use is simple, but efficient: • WHY: Focus on the “why” first so you understand what you’re doing. • HANDS-ON lessons to learn in a better way. • STEP BY STEP: Every lesson is built on top of previous ones. • PRACTICE with many activities and projects. • NO COPY AND PASTE!!! I write all the code from scratch. This teaching method actually works: I have already taught 100 000+ students in 150 countries, with 25 000+ reviews over 4.7/5. And don’t take my word for it - here are some reviews from my students on Udemy: “Simple and great course, learning while doing.” — Jose Almeida “Halfway and still loving this course. The instructor is clear and teaches in such a way that you actually learn how to navigate, conceptualize, and write your own code rather than teaching from premade code.” — Westley Bolton “Really happy with the course. Exactly what I paid for and am pretty happy with my progress. I am getting exactly what is promised. Edouard is an excellent teacher.” — Jihad Anbous “Really step by step. It’s what I was needing. Extremely clear, with no blind spots in the lectures. Definitely worth it.” — Nicolas Hernandez → What will you do in the course? You will first learn about the programming process - how to: • Define what problem you’re really trying to solve. • Create an algorithm to solve that problem. • Implement the algorithm with code (Scratch and Python). Then we will apply this coding process in many activities and projects, step by step, while learning the coding fundamentals: variables, conditions, lists, loops, functions, libraries, etc. For each coding fundamentals, you will get: • Real life analogy to explain the “why”. • Flowchart programming to create algorithms using this concept. • Block programming to start implementing the algorithm with code (in an easy way). • Text programming with Python (“real” programming). • An activity directly related to the concept you’ve just learned. After each coding concept, you will create a complete project to build a real app, which will allow you to consolidate your knowledge by practicing on the new concept you’ve discovered, combined with all the previous concepts you’ve learned in the course. → Start to code Now! So, do you want to finally become a developer and understand what you’re doing? Enroll in this course and start your programming journey TODAY! After this course, you will feel much more confident when writing code. You will really understand the “why” behind what you’re doing, and also you will have a much better idea of what you can do next. That’s not all: right from the start you will get the best practices I personally use when coding. My goal is not to make you become good, I want to make you become great at what you do. You also get a 30 days money-back guarantee if you’re not fully satisfied. So just get started now, and if I don’t deliver on what I said, feel free to get the refund without having to justify yourself. No risk here, only a great opportunity to learn a life-changing skill. This course is eligible for the Codestars Certificate Authority (CCA) certificate. Students can take the official exam via codestarscom, and those who pass the quiz will receive their CCA certificate. (more details in the course!) See you in the course! :)
Generative AI in Healthcare: Practical & Hands-on Learning
Udemy · 64.99 USD · ⭐ 4.44 · ⏱ 2,5 h · Inglés
Explore the transformative power of Generative AI in Healthcare with this hands-on, practical course. From medical imaging to disease prediction and personalized treatment, generative AI is revolutionizing healthcare, and this course will give you the skills to be part of that change. You’ll start by understanding the fundamentals of generative AI and deep generative models, including Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). Through practical examples, you’ll learn how these models are applied to healthcare problems, from analyzing medical images to generating synthetic patient data. The course also covers critical topics like patient data privacy, ethical AI, and regulatory compliance, ensuring that your AI solutions are safe, responsible, and compliant with healthcare standards. You’ll implement privacy-preserving techniques and explore bias mitigation strategies to make your models fair and ethical. Hands-on projects guide you through building real-world AI applications, including medical imaging analysis, disease prediction models, and personalized treatment recommendations. Advanced topics such as transfer learning, hyperparameter tuning, interpretability, and model deployment are also covered, giving you the skills to take your models from development to production. By the end of this course, you’ll have the knowledge and practical experience to design, build, and deploy generative AI solutions in healthcare, ready to contribute to cutting-edge projects that improve patient outcomes. No prior healthcare experience is required—just a foundation in AI or Python programming and a desire to learn.
The AI Agents Bootcamp 2026: Complete AI Аgents Course
Udemy · 19.99 USD · ⭐ 4.43 · ⏱ 12 h · Inglés
The Problem Agentic AI is the future of AI-powered organizations. It helps businesses innovate faster than ever before. Therefore, it’s not surprise that the demand for AI Agent Engineers has been surging in the job marketplace. Supply, however, has been minimal, and acquiring the skills necessary to be hired as an AI Agent Engineer can be challenging. So, how is this achievable? Universities have been slow to develop specialized programs focused on practical AI agent engineering skills. The few attempts that exist are expensive and time-consuming. At the same time, most online courses offer high-level walkthroughs of individual techniques for building agentic systems, yet integrating these skills remains challenging. The Solution AI agent engineering is a multidisciplinary field covering: • AI agent foundations • AI agent design and architecture • Python programming • Working with low-code automation platforms like n8n • AI agent optimization for speed and cost • Connecting agents to tools, memory, and APIs with LangChain • Model AI agent workflows with LangGraph • AI agent evaluation with LangSmith • Applying agents to real-world problems • Launching and optimizing agents in production Each topic builds on the previous one, and skipping steps can lead to confusion. For instance, optimizing agent performance without a fundamental understanding of agent architecture is rarely achievable. So, we created the AI Agent Engineer Bootcamp 2026 to provide the most effective, time-efficient, and structured AI agent training available online. This pioneering training program overcomes the most significant barrier to entering the AI agent field by consolidating all essential resources in one place. Our course is designed to teach interconnected topics seamlessly—providing all you need to become an AI agent engineer at a significantly lower cost and time investment than traditional programs. The Skills 1. Intro to AI Agents Decision-making logic, actuators, updated environment, single agents, multi-agents, guardarails—there are familiar AI agent buzzwords; what exactly do they mean? Why study AI agent basics? Build a solid foundation that will support your learning journey. Understand the big picture and how different building blocks fit together. 2. AI Agent Architecture We build AI agents to solve problems. Each problem requires the right architecture and an understanding of the trade-offs involved. Why study AI agent architecture? The system design choices you will make will determine how effective and efficient your AI agents are. By mastering classic AI agent architecture you will be able to make confident choices at the system design stage—before problems become costly to fix. 3. Building AI Applications with LangChain LangChain is a framework that allows for seamless development of AI-driven applications by chaining interoperable components. Why study LangChain? Learn how to create agents that can reason. LangChain facilitates the creation of systems where individual pieces—such as language models, databases, and reasoning algorithms—can be interconnected to enhance overall agent functionality. 4. LangGraph LangGraph sets the foundation of how we can build and scale AI workloads. Use this tool to design agents that reliably handle complex tasks. Why study LangGraph? With LangGraph you will be introduced to multi-step agent orchestration. This is where you learn how to add conversational memory to your agent, so it learns to remember, adapt, and grow smarter with every interaction. 5. AI Agents in Practice Step into the world of AI agents with this practical module on agentic systems. You will gain real-world experience. From prompt design and multi-step reasoning to safety techniques and LangSmith monitoring. Why study AI Agents in Practice? Gain the practical skills to build production-ready AI workflows. Take the next step in your AI journey with hands-on projects. What You Get • $1,250 AI agent engineering training program • Active Q&A support • Essential skills for AI engineering employment • AI learner community access • Completion certificate • Real-world business case solutions for job readiness We're excited to help you become an AI Agent Engineer from scratch—offering an unconditional 30-day full money-back guarantee. With excellent course content and no risk involved, we're confident you'll love it. Why delay? Each day is a lost opportunity. Click the ‘Buy Now’ button and join our AI Agent Engineer program today.
Machine Learning con Python. Aprendizaje Automático Avanzado
Udemy · 64.99 USD · ⭐ 4.43 · ⏱ 28 h · Español
Machine Learning con Python. Aprendizaje Automático Avanzado Aprendizaje Automático Scikit-Learn en Python. Modelos Predictivos. Data Science. De básico a Experto. Instructores: PhD. Manuel Castillo y Arquitecto Álvaro García. Requisitos: Se recomienda tener conocimientos de programación, preferiblemte Python. Descripción del Curso: El curso de “Machine Learning con Python” se centra en un subcampo específico de aprendizaje automático llamado modelado predictivo y clustering. Este es el campo del aprendizaje automático que es el más útil en la industria y el cual se utilizar la librería de aprendizaje automático scikit-learn en Python por su gran rendimiento y facilidad en su uso. A diferencia de las estadísticas, donde los modelos se usan para comprender los datos, el modelado predictivo se enfoca en el desarrollo de modelos que hacen las predicciones más precisas a expensas de explicar por qué se hacen las predicciones. A diferencia del campo más amplio del aprendizaje automático que podría utilizarse con datos en cualquier formato, el modelado predictivo y clustering se centra principalmente en datos tabulares, llamados técnicamente Tidy Data (por ejemplo, tablas de números como en una hoja de cálculo). El curso está dirigido a personas que tengan pocos conocimientos de machine learning, conocimientos intermedios del lenguaje de programación y que quieran adentrarse a este apasionante mundo de dentro del campo de modelado predictivo y clustering. Además, el curso está diseñado para que cualquier estudiante universitario, investigador o tecnólogo que se encuentre realizando o necesite realizar diferentes experimentos a través de grandes conjuntos de datos para poder sintetizarlos en alguna salida predictiva puedan utilizar los muy diferentes recursos de machine learning que nos pone a nuestra disposición el lenguaje de programación. Contenidos del Curso: MÓDULO I. Introducción. • Conceptos básicos de machine learning. • Jupyter Notebook como nuestro entorno de machine learning. • Curso rápido de Python. MÓDULO II. Análisis de datos • Cargar un conjunto de datos. • Estadística descriptiva. • Visualización de datos. • Taller: Trabajo de aplicación de diferentes técnicas analíticas de datos en un conjunto de datos seleccionado por el usuario e interpretar la salida obtenida. • Examen tipo test sobre los contenidos del módulo. MODULO III. Preprocesamiento de datos • Análisis exploratorio de datos. • Preprocesamiento de datos. • Métodos de remuestreo para estimar la precisión del modelo. • Taller: Trabajo de aplicación de diferentes técnicas de análisis y procesamiento de datos de datos en un conjunto de datos seleccionado por el usuario e interpretar la salida obtenida. • Examen tipo test sobre los contenidos del módulo. MÓDULO IV. Fase de tratamiento de datos • Evaluación de las métricas. • Feature Selection. • Feature Importance. • Reducción de dimensiones en un dataset. • Taller: Aplicación de diferentes técnicas de tratamiento de datos en un conjunto de datos y verificación de su impacto en las métricas algorítmicas. • Examen tipo test sobre los contenidos del módulo. MÓDULO V. Fase de modelado • Algoritmos de Machine Learning. • Rendimiento de los algoritmos. • Algoritmos Ensamblados • Algoritmo "Super Lerner" • Taller: Aplicación de diferentes algoritmos de machine learning en un conjunto de datos e interpretar la salida obtenida, así mismo, verificar el algoritmo que tenga mejor comportamiento. • Examen tipo test sobre los contenidos del módulo. MÓDULO VI. Fase de optimización y forecasting • Pipelines. • Procesamiento de datos avanzado. • Configuración de hiperparámetros. • Guardado e integración del modelo. • Taller: Una vez seleccionados los algoritmos candidatos a modelo realizar una optimización de estos a través de la configuración de sus hiperparámetros. • Examen tipo test sobre los contenidos del módulo. MÓDULO VII. Proyectos de machine learning • Trabajar un proyecto de clasificación multiclase • Trabajar un proyecto de regresión. • Trabajar un proyecto de clasificación binaria. • Proyecto: Realizar un proyecto completo analizando todas las fases estudiadas en los diferentes módulos. MÓDULO VIII. Aprendizaje No Supervisado • Aprendizaje No supervisado. • Algoritmos de Aprendizaje No Supervisado. • Determinar el número óptimo de clústers. • Proyecto de Aprendizaje No Supervisado. Características del Curso: Recuerda que esta formación incluye lecciones en vídeo fullHD con audio de estudio (compatible con TV, PC, Mac, tablet y smartphone), artículos didácticos, actividades, proyectos paso a paso, recursos descargables, links de interés, acceso de por vida, certificado de finalización, tutorización online, y una exclusiva comunidad de aprendizaje privada que nos ayudamos aportando nuestras experiencias en el foro de comunicación del curso. Con esta formación disfrutarás aprendiendo desde dónde quieras, sin tener que desplazarte, sin horarios, con quién quieras, según tus necesidades y disponibilidad. Aprenderás con un instructor avalado por miles da alumnos satisfechos en todo el mundo (comentarios certificados). Conocerás las técnicas, métodos, trucos y flujos de trabajo de este sector creativo. El docente te transmitirá su sabiduría y conocimientos con pasión a la vez que las explicaciones concisas, claras, sencillas y con un enfoque profesional en cada clase. Podrás conseguir un certificado homologado personalizado y firmado por tu instructor en cada formación. De está forma podrás compartir tu título en tu portafolio, currículo, en redes sociales... Con la alta definición de los vídeos (vídeo fullHD y audio de estudio) conseguirá no te perder detalle. Podrás ver las clases las veces que requieras para recordar y perfeccionar tus habilidades como diseñador. Tendrás la posibilidad de preguntar, pedir opinión y ayuda al instructor, además de compartir tu experiencia de aprendizaje con los demás alumnos del curso, tan apasionados como tú, repartidos por todo el mundo. Seleccionamos cuidadosamente los contenidos y producimos cada curso para garantizar una experiencia de aprendizaje online integral y de la máxima calidad. ¿A qué esperas?, este curso es ideal para ti, atrévete a convertirte en un experto. Adelante, nos vemos dentro de la formación ;)