Cursos de Datos e IA
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Process Images & Extract Motion Features
Master the fundamental preprocessing techniques that power modern computer vision systems. Raw visual data is everywhere, but transforming it into actionable insights requires precise preprocessing and motion analysis skills that separate successful AI engineers from the rest. This Short Course was created to help machine learning and AI professionals accomplish systematic image preprocessing and motion feature extraction for computer vision applications. By completing this course, you'll be able to standardize image data through normalization techniques, convert between color spaces for optimal model performance, and extract motion patterns from video sequences using industry-standard algorithms. These skills directly translate to building more robust computer vision models, improving training efficiency, and developing motion-based applications. By the end of this course, you will be able to: • Apply normalization and color-space conversions to preprocess image data • Apply optical flow and frame differencing techniques to extract motion features from video This course is unique because it combines theoretical understanding with hands-on implementation using real-world datasets, mirroring the exact preprocessing pipelines used by companies like Tesla, Facebook AI Research, and Amazon for their computer vision systems. To be successful in this project, you should have a background in Python programming, basic understanding of machine learning concepts, and familiarity with NumPy and OpenCV libraries.
Generative AI and Prompt Engineering Essentials
This course offers a clear pathway to understand Generative AI and prompt engineering—two foundational skills for working with modern large language models (LLMs). You'll learn how models like GPT, BERT, and T5 generate human-like outputs, and how well-crafted prompts can guide these models to perform tasks across writing, coding, summarization, and more. Through hands-on exercises and real-world examples, you’ll build the skills to communicate effectively with AI systems, enhance generation quality, and apply responsible prompting strategies across diverse applications. By the end of this course, you will be able to: - Explain how transformer-based models like GPT, BERT, and T5 work and compare their capabilities - Design prompts for various tasks using zero-shot, one-shot, and few-shot techniques - Apply advanced strategies such as Chain-of-Thought, Tree-of-Thought, and knowledge-grounded prompting - Identify and defend against prompt injection and adversarial threats - Evaluate AI outputs using metrics like BLEU, ROUGE, and human-AI collaboration strategies This course is ideal for developers, data scientists, content creators, and early-career AI practitioners aiming to build effective, safe, and scalable Generative AI solutions. No prior experience with LLMs is required, though a basic understanding of Python and machine learning concepts is helpful. Join us to master the essential techniques behind today’s most powerful AI tools—and shape the future with your prompts.
Transform and Communicate AI Insights Visually
Transform and Communicate AI Insights Visually is an intermediate course designed to help learners turn raw data into clear, actionable stories that drive smarter decisions. You’ll explore how to prepare, join, and aggregate CRM and usage tables to build reliable analytical foundations using SQL and Pandas. From there, you’ll learn to evaluate findings against hypotheses, visualize funnel performance, and craft concise insight messages that stakeholders understand instantly. Through hands-on practice, real-world scenarios, and interactive exercises, you’ll strengthen both your technical transformation skills and your ability to communicate patterns with clarity and confidence. By the end of the course, you’ll know how to structure data-driven narratives that illuminate user behavior, highlight drop-off points, and support informed decision-making across teams.
Generative AI Part 2
Introduces the theoretical foundations and advanced concepts of neural networks, generative models, transformers, and large language models. Students will explore how these AI systems create new data, process information, and learn through feedback, while analyzing their applications across various fields. The course emphasizes key principles in model building, optimization, and real-world generative AI use cases.
معالجة البيانات وتحليل الأعمال باستخدام برنامج جدول البيانات
في نهاية هذا المشروع ، ستكون قادرًا على معالجة البيانات بالدوال، وتطبيق المعادلات لاستخراج الكلمات من النص. علاوة على ذلك ، سوف تكون قادرًا على تحديد وإدارة البيانات الخاصة بك. أخيرًا ، ستتمكن من إنشاء جداول الاحصائيات ومعادلات البحث لتلخيص بياناتك وتحليلها. سيوضح لك هذا المشروع كيفية التعامل مع النص وكيفية تحليله لتقديمه بشكل احترافي بطرق مختلفة. هذا المشروع مخصص للأشخاص في مجال الأعمال وتحليل البيانات. يوفر لكم الخطوات المهمة لتكون محلل بيانات. علاوة على ذلك ، فإنه يزودك بالمعرفة الموجودة في جداول البيانات المتعلقة بتحليل الأعمال وكيفية التعامل مع البيانات حسب المعادلات.
Databricks to Local LLMs
By the end of this course, a learner will master Databricks to perform data engineering and data analytics tasks for data science workflows. Additionally, a student will learn to master running local large language models like Mixtral via Hugging Face Candle and Mozilla llamafile.
Analyze Business Data Using Tableau for Decision Making
Learners will analyze business data, apply business intelligence concepts, and create interactive visualizations and dashboards using Tableau. By the end of this course, learners will interpret data structures, apply analytical functions, design effective charts, and forecast trends to support data-driven decision-making. This course provides a structured, end-to-end learning journey through Tableau, starting with core Business Intelligence fundamentals and progressing to advanced data analysis and visualization techniques. Learners gain hands-on exposure to Tableau architecture, data connections, metadata, joins, calculations, filters, and dashboard design best practices. Each module builds practical skills required to transform raw data into meaningful insights that address real business questions. What makes this course unique is its strong emphasis on analytical thinking combined with visual storytelling. Rather than focusing solely on tool usage, the course guides learners through the complete BI workflow—from understanding data and preparing it correctly to selecting the right visualizations and communicating insights effectively. This course is ideal for aspiring business analysts, data analysts, and professionals seeking to strengthen their Tableau and business intelligence capabilities for real-world applications.
Build & Optimize TensorFlow ML Workflows
This short course helps you build and optimize machine learning workflows using TensorFlow 2.x. You’ll start by structuring an end-to-end pipeline that includes data ingestion with tf.data, model definition with Keras, and custom training with checkpointing for reliability. You’ll then learn how to optimize your models for deployment using TensorFlow Lite, including post-training quantization and latency benchmarking. Along the way, you’ll see how ML engineers measure performance, evaluate tradeoffs, and deploy models to mobile and edge devices. Through hands-on practice and real-world examples, you’ll learn to think like an applied ML practitioner who builds efficient, production-ready TensorFlow systems.
Deep Learning with PyTorch
Get hands-on experience in building and deploying intelligent systems using PyTorch by using one of the most widely used deep learning frameworks in AI development. In this practical course, you’ll gain job-ready skills in deep learning, machine learning, and neural networks, boosting your resume for roles like AI Engineer, Machine Learning Engineer, and Data Scientist. During the course, you’ll implement logistic regression and softmax regression, train deep neural networks, and build convolutional neural networks (CNNs) for real-world image classification tasks. You’ll master core techniques such as gradient descent, backpropagation, and cross entropy loss, while improving performance with weight initialization, dropout regularization, and batch normalization. Additionally, you’ll leverage GPU acceleration, perform hyperparameter tuning, and apply transfer learning using pretrained models such as ResNet18. Finally, you’ll complete a project, where you’ll design, train, and evaluate models using modern model optimization and data preprocessing workflows. Great to talk about in interviews! Enroll today to accelerate your career in deep learning, AI, and machine learning.
Analyze and Visualize Data Using Tableau Desktop
By the end of this course, learners will be able to analyze data using Tableau Desktop, create effective visualizations, build interactive worksheets and dashboards, and present insights through structured data stories. This course provides a comprehensive, hands-on journey through Tableau Desktop, starting from core concepts and progressing to advanced analytics and presentation design. Learners will explore how Tableau is used in real business contexts, understand how to connect and work with data, and develop strong visualization skills using charts, filters, analytics features, and interactive elements. The course also emphasizes dashboard creation, storyboard design, and best practices for organizing and managing Tableau workbooks. Learners benefit by gaining practical, job-ready skills that support data-driven decision making across industries. Each module builds progressively, ensuring concepts are reinforced through structured learning and application-focused assessments. What makes this course unique is its end-to-end coverage of Tableau Desktop—from foundational understanding to advanced storytelling—combined with a strong focus on usability, clarity, and analytical thinking rather than tool navigation alone. The course is ideal for professionals, analysts, and students seeking to confidently transform data into clear, actionable insights using Tableau.
Artificial Intelligence in Bioinformatics
This course provides a comprehensive introduction to the application of artificial intelligence in bioinformatics, bridging computational methods with biological data analysis. Using Weka, a widely adopted machine learning software suite, learners will gain hands-on, practice-oriented experience alongside a solid theoretical foundation. Learners will explore the four core branches of bioinformatics—sequence analysis, structural bioinformatics, gene and protein expression, and network and systems biology—while gaining experience with widely used public bioinformatics databases. The course then covers the fundamentals of machine learning and deep learning, including data preparation, feature extraction, model evaluation, and key algorithms such as K-Nearest Neighbors, Random Forest, and Support Vector Machines. Through practical exercises in Weka and WekaDeeplearning4j, learners will build, tune, and evaluate predictive models for real-world bioinformatics problems, including protein function prediction and electron transport protein classification, using techniques such as Convolutional Neural Networks and Recurrent Neural Networks. By the end of this course, learners will be equipped with both the theoretical foundation and practical skills needed to apply AI-driven approaches to genomics and proteomics research, and to communicate their findings through effective scientific writing.
Operationalizing ML Models: MLOps for Scalable AI
In this course you’ll explore how to turn promising ML prototypes into robust, scalable, and maintainable systems that deliver real value. Through hands-on demos, practical tools, and real-world case studies from companies like Netflix, Uber, and Google, you’ll gain a comprehensive understanding of what it takes to run ML systems effectively in production using MLOps. This course is designed for data scientists, machine learning engineers, AI practitioners, and IT professionals who want to operationalize machine learning workflows, scale AI systems, and streamline deployment and infrastructure management. To get the most out of this course, learners should have a basic understanding of machine learning concepts, be familiar with Python programming, and have experience using Docker and containerization technologies. By the end of this course, learners will be able to operationalize machine learning models by designing scalable MLOps workflows, automating deployments with CI/CD pipelines, monitoring performance and detecting data drift, and optimizing AI infrastructure using tools like Docker, MLflow, and Kubernetes to support robust, real-world AI applications.
Optimize Your Marketing Pipeline with AI
Did you know that businesses using AI-powered lead management experience up to 50% higher sales productivity? Automating lead tracking and funnel optimization can transform how marketing teams drive revenue. This Short Course was created to help professionals in this field master lead management automation and performance funnel optimization to drive revenue growth and operational efficiency. By completing this course, you will be able to use CRM tools to manage leads, automate updates, and analyze funnel performance to identify bottlenecks and improve conversion rates—skills you can immediately apply to create smarter, faster marketing pipelines. By the end of this 3-hour long course, you will be able to: Apply CRM capabilities to import new leads, update their status, and create associated tasks. Analyze a performance funnel report to calculate conversion rates and identify bottlenecks. This course is unique because it combines AI-driven automation with practical CRM strategies, teaching you how to optimize every stage of your marketing funnel for greater visibility, agility, and measurable growth. To be successful in this project, you should have: Basic digital marketing knowledge CRM platform familiarity Understanding of sales processes Spreadsheet management skills
Customer Segmentation with K-Means: Model & Visualize
Unlock the power of customer segmentation by learning how to analyze, model, and visualize shopping behavior using Python and K-Means clustering. In this hands-on course, you'll work through a practical workflow that transforms customer data into meaningful business insights using unsupervised machine learning techniques. You'll begin by preparing customer datasets, configuring your analysis environment, and creating visualizations such as pie charts, histograms, violin plots, and pair plots to explore customer characteristics. Next, you'll examine relationships between variables through correlation analysis, prepare data for clustering, and build a K-Means model. Finally, you'll visualize customer clusters, evaluate segmentation results, and interpret shopping behavior to support informed marketing and business decisions. Designed for learners interested in data analysis, machine learning, and customer analytics, this course emphasizes a structured, end-to-end approach—from data exploration and preprocessing to clustering and insight generation. By combining visualization, modeling, and cluster interpretation within a single learning experience, you'll gain practical skills for analyzing customer behavior and identifying meaningful customer segments using real-world data. Enroll to develop a systematic approach to customer segmentation and learn how data-driven analysis can support more informed business strategies.
Automate, Analyze, and AI Feedback
Automate, Analyze, and AI Feedback is an intermediate-level course for MLOps professionals and data scientists who need to build AI systems that do not just launch, but last. In the real world, even the best models degrade over time due to model drift. This course teaches you to combat this by creating automated, self-improving systems that learn from operational experience. You will learn to design and deploy Human-in-the-Loop (HITL) pipelines that identify low-confidence predictions, route them for expert human review, and schedule automated retraining with the new, high-quality data. Moving beyond simple accuracy, you will master advanced model evaluation techniques. Through hands-on labs, you will generate and analyze Precision-Recall (PR) curves, apply resampling methods to ensure your model generalizes well, and select the optimal decision threshold that balances competing business objectives, like maximizing recall while minimizing false alarms. This course will equip you to build resilient MLOps systems that turn human expertise into a continuous source of model improvement.
Data Science for Business Innovation
This is your chance to learn all about Data Science for Business innovation and future-proof your career. Match your business experience tech and analytics! The Data Science for Business Innovation nano-course is a compendium of the must-have expertise in data science for executives and managers to foster data-driven innovation. The course explains what Data Science is and why it is so hyped. You will learn: * the value that Data Science can create * the main classes of problems that Data Science can solve * the difference is between descriptive, predictive, and prescriptive analytics * the roles of machine learning and artificial intelligence. From a more technical perspective, the course covers supervised, unsupervised and semi-supervised methods, and explains what can be obtained with classification, clustering, and regression techniques. It discusses the role of NoSQL data models and technologies, and the role and impact of scalable cloud-based computation platforms. All topics are covered with example-based lectures, discussing use cases, success stories, and realistic examples. Following this nano-course, if you wish to further deepen your data science knowledge, you can attend the Data Science for Business Innovation live course https://professionalschool.eitdigital.eu/data-science-for-business-innovation
Le nettoyage de données
Il s’agit du quatrième cours du Google Data Analytics Certificate. Ces cours vous permettront d’acquérir les compétences dont vous avez besoin pour postuler les emplois d’analyste de données de niveau junior. Dans ce cours, vous continuerez à approfondir votre compréhension de l'analytique des données et des concepts et outils utilisés par les analystes de données dans leur travail. Vous apprendrez comment vérifier et nettoyer vos données à l'aide de feuilles de calcul et de SQL, ainsi que comment vérifier et créer des rapports de vos résultats de nettoyage de données. Des analystes de données actuellement chez Google vous instruiront et vous fourniront des moyens pratiques d’accomplir les tâches courantes des analystes de données, avec les meilleurs outils et ressources. Les participants qui terminent cette formation certifiante seront préparés à postuler à des emplois d’analyste de données de niveau junior. Aucune expérience préalable n’est nécessaire. À la fin de ce cours, vous aurez : - Appris comment vérifier l'intégrité des données. - Découvert les techniques de nettoyage de données à l'aide de feuilles de calcul - Développé des requêtes SQL de base pour une utilisation sur les bases de données. - Appliqué les fonctions SQL de base pour le nettoyage et la transformation des données. - Compris comment vérifier les résultats du nettoyage de données. - Exploré les éléments et l'importance des rapports de nettoyage de données.
Using BigQuery in the Google Cloud Console
This is a self-paced lab that takes place in the Google Cloud console. This lab shows you how to query public tables and load sample data into BigQuery using the GCP Console. Watch the following short video Get Meaningful Insights with Google BigQuery.
SQL for Beginners: Fundamentals & Database Setup
If you work with data and have never touched SQL, you are leaving one of the most in-demand skills on the table. This course changes that: from database basics to writing real queries, from scratch. Here is what you will build: • SQL and Database Foundations Understand what SQL and relational databases are. Explore database systems across MySQL, PostgreSQL, SQLite, Oracle, and more. • Database Setup and Environments Build a working SQL environment on your machine. Install MySQL, SQLite, and PostgreSQL on Windows and macOS, configure MySQL Workbench and pgAdmin, and run real queries. • Database Design & Normalization Apply 1NF, 2NF, 3NF, and BCNF to eliminate redundancy & build schemas that also stay clean as data grows. • ER Diagrams & Relationships Map entities, attributes, keys, & relationship types. Apply ON DELETE rules across one-to-many and many-to-many relationships. • SQL Querying in Practice Write queries by making use of WHERE, ORDER BY, LIMIT, & DISTINCT. Filter with BETWEEN, IN, IS NULL, and LIKE on real tables. Taught in Hindi and ideal for anyone starting a data career. 160+ LearnKartS courses have shaped 200,000+ careers. Start yours now.
Excel Data Analysis and DAX Essentials Course
This comprehensive Excel Data Analysis with DAX course equips you with the skills to transform raw data into actionable insights using powerful Excel functions and DAX formulas. Begin by mastering DAX fundamentals—understand how DAX works in Excel to build dynamic and interactive reports. Learn to enhance your spreadsheets by inserting checkboxes, PDFs, images, and PowerPoint content. Customize your reports with tick marks, barcodes, watermarks, and optimized cell formatting. Progress to advanced Excel features—use Flash Fill, Index Match, round-off, and statistical functions for efficient data processing. Organize and visualize data with precision using AutoSum, filters, slicers, and sorting techniques. You should have a basic understanding of Excel operations such as formulas, cell formatting, and spreadsheet navigation. By the end of this course, you will be able to: - Understand DAX: Leverage DAX formulas for advanced Excel calculations - Enhance Excel: Add checkboxes and external content to enrich reports - Transform Data: Use advanced functions for accurate data processing - Visualize Clearly: Apply sorting, slicers, and formatting for better insights Ideal for analysts, marketers, and professionals looking to master data-driven decision-making with Excel.
Optimize Java Memory for ML Performance
Memory inefficiencies cause 40% of Java ML application performance problems, making optimization critical for production systems. This course equips Java developers to build memory-efficient ML systems through hands-on profiling with Java Flight Recorder and systematic optimization of collections and JVM settings. You'll diagnose bottlenecks using heap analysis, optimize pipelines by replacing inefficient structures like LinkedList with ArrayDeque, and tune garbage collectors for low-latency inference. This course eliminates memory bottlenecks, degrading ML production systems. With hands-on labs, you will simulate production scenarios, including GC pause analysis and container optimization. This course is for Java developers, ML engineers, and backend professionals looking to boost performance, reduce latency, and optimize memory in production ML systems. Learners should know Java, JVM basics, and collections, with command-line skills and familiarity with ML pipelines and build tools like Maven or Gradle. By course completion, you'll identify allocation hotspots, reduce GC overhead by 30%+, configure JVM for sub-100ms latency, and deploy optimized containerized ML services.
Full-Stack AI and Mastering AI Agents Bootcamp
This course 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. In this bootcamp, you’ll learn full-stack AI development, starting with setting up Ollama and building your first AI models. Explore AI for text processing, including content generation, grammar checking, and text summarization with NLP and large language models. You’ll also build AI-powered conversational bots and assistants, and create real-world applications like web scrapers and document readers. Integrate memory, voice recognition, and web interfaces to develop complex AI agents. This course is ideal for developers and aspiring AI engineers looking to deepen their skills in AI agent development. Prerequisites include basic knowledge of Python and AI concepts. By the end, you’ll have hands-on experience creating and deploying advanced AI models and systems for real-world projects.
Introduction to Python Fundamentals
How many times have you decided to learn a programming language but got stuck somewhere along the way, grew frustrated, and gave up? This specialization is designed for learners who have little or no programming experience but want to use Python as a tool to play with data. The first course will introduce you to programming languages, with Python as an example. You are going to learn how to use variables and operators, as well as input/output and flow controls to build simple Python programs. The pace will be very slow, so you will feel comfortable learning Python as quickly or as slowly as you like. Are you ready? Let's go! Logo image courtesy of Mourizal Zativa. Available on Unsplash here: https://unsplash.com/photos/gNMVpAPe3PE
Building Modern Data Applications Using Databricks Lakehouse
In today’s data-driven world, building scalable and efficient data applications is crucial for staying ahead in business and technology. This course explores the power of Databricks Lakehouse, a unified platform for managing and analyzing large volumes of data, and guides you through essential skills to create modern data applications. Throughout the course, you’ll learn to work with Delta Live Tables for data transformation, management, and quality assurance. You will also dive deep into Databricks’ Unity Catalog for enhanced governance, data lineage, and location management. The hands-on experience with deploying and maintaining DLT pipelines using Terraform prepares you for real-world data infrastructure challenges. This course stands out by combining theoretical understanding with practical, real-world applications. You’ll gain a robust set of skills in data pipeline management, governance, and monitoring, preparing you for building production-level data applications with Databricks Lakehouse. Designed for professionals looking to deepen their expertise in modern data architecture, this course is suitable for data engineers, data scientists, and IT professionals who want to leverage Databricks to solve real-world data problems.
Mastering PROC SQL in SAS: Analyze, Query & Optimize Data
Learners will build, query, analyze, optimize, and maintain SAS datasets using PROC SQL. They will apply joins, construct complex queries, generate reports, automate workflows with macro variables, enforce data integrity with constraints, and troubleshoot performance issues across real-world data scenarios. This comprehensive course equips learners with the practical skills needed to work confidently with SQL inside the SAS environment. Throughout the course, learners benefit from hands-on demonstrations, clear explanations of SQL logic, and structured modules that progressively develop beginner to advanced capabilities. By the end, learners will be able to transform raw datasets into reliable, meaningful insights using professional SQL techniques. What makes this course unique is its complete coverage of PROC SQL—from foundational SELECT statements to advanced subqueries, indexing strategies, metadata manipulation, and matrix-style analytical logic rarely covered in traditional SQL training. The combination of SAS-focused best practices, real examples, and step-by-step problem-solving ensures that learners not only understand how PROC SQL works but also why certain techniques produce better accuracy, performance, and scalability.
GenAI Model Development and Production Engineering
Frustrated with AI models that can't understand your specific domain or scale beyond demo environments? Most organizations struggle to transform promising AI prototypes into robust, production-ready systems that deliver consistent value under real-world enterprise demands, leaving breakthrough potential unrealized. This comprehensive GenAI Model Development and Production Engineering course transforms you into a complete GenAI specialist who can fine-tune foundation models for specialized domains, architect resilient deployment infrastructure, and maintain GenAI models in production that scale reliably to millions of users. You'll gain a deep understanding of the GenAI development process, mastering advanced fine-tuning techniques including parameter-efficient methods such as LoRA, implementing enterprise-grade deployment strategies with comprehensive monitoring and automated maintenance, and building production systems using advanced optimization techniques such as semantic caching, hybrid routing, and edge deployment. This course is designed for professionals engineering AI systems at scale, including ML engineers building production-ready GenAI models, DevOps engineers managing GenAI production engineering workflows, platform engineers developing scalable AI infrastructure, and technical architects designing end-to-end enterprise AI solutions. Whether you're optimizing model performance, deploying large language models, or ensuring GenAI in production operates reliably across cloud environments, this course equips you with practical skills to deliver secure, scalable, and high-performance AI systems. Participants should have completed foundational courses in generative AI, data engineering, and AI agent development. Proficiency in advanced Python programming and experience with machine learning frameworks are essential. Learners should also have hands-on familiarity with cloud platforms, Docker, Kubernetes, and the model development process, including model training, evaluation, deployment, and production system architecture. Prior experience with GenAI model development or MLOps concepts will help learners maximize the value of this course. By the end of this course, learners will be able to execute advanced GenAI model development workflows, including LoRA-based fine-tuning and domain-specific model adaptation. They will implement enterprise-grade GenAI production engineering strategies with automated deployment, monitoring, container orchestration, and scalable infrastructure. Additionally, learners will build robust production monitoring systems with real-time alerting and apply advanced optimization techniques including semantic caching, hybrid routing, and edge deployment to deliver reliable, resilient, and production-ready generative AI systems.
AI For Medical Treatment
AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine. Medical treatment may impact patients differently based on their existing health conditions. In this third course, you’ll recommend treatments more suited to individual patients using data from randomized control trials. In the second week, you’ll apply machine learning interpretation methods to explain the decision-making of complex machine learning models. Finally, you’ll use natural language entity extraction and question-answering methods to automate the task of labeling medical datasets. These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend that you take the Deep Learning Specialization.
The Ultimate SQL Bootcamp: Zero to Hero in 9 Hours
This course 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. Get hands-on with SQL and master one of the most in-demand skills in data and software development. In this intensive bootcamp, you'll learn how to create, query, and manage databases using SQL Server, going from complete beginner to confident practitioner in just a few hours. The course begins with foundational concepts, including database architecture, installation setup, and sample data import. You'll then dive into the essential SQL commands—DDL and DML—for building and manipulating databases. Topics like data integrity, filtering, joins, and built-in functions are explained clearly, with frequent practice opportunities. As your skills grow, you'll explore more advanced techniques like subqueries, CTEs, stored procedures, and window functions. The course wraps up with a real-world capstone project analyzing EuroMart sales data—bringing all your new skills together in one cohesive challenge. Perfect for aspiring data analysts, developers, and business professionals, this course assumes no prior experience. If you’re looking to quickly build SQL skills from scratch or solidify your knowledge with real projects, this beginner-friendly course is the ideal place to start.
Gen AI Dev- Safe User Interactions with Gen AI Applications
Welcome to the third part of this learning plan centered around the role and responsibilities of a generative AI professional developer.In part two, you learned how to implement Agentic AI solutions and tools. You also learned about AI agents, how they work, make decisions, and take actions.In this part, you'll learn about security, governance, and compliance when working with your AI applications, and work with AI-assisted development tools.
The Nuts and Bolts of Machine Learning
This is the fifth course in the Google Advanced Data Analytics Certificate. In this course, you’ll learn about machine learning, which uses algorithms and statistics to teach computer systems to discover patterns in data. Data professionals use machine learning to help analyze large amounts of data, solve complex problems, and make accurate predictions. You’ll focus on the two main types of machine learning: supervised and unsupervised. You'll learn how to apply different machine learning models to business problems and become familiar with specific models such as Naive Bayes, decision tree, random forest, and more. Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. Learners who complete the eight courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate. By the end of this course, you will: -Apply feature engineering techniques using Python -Construct a Naive Bayes model -Describe how unsupervised learning differs from supervised learning -Code a K-means algorithm in Python -Evaluate and optimize the results of K-means model -Explore decision tree models, how they work, and their advantages over other types of supervised machine learning -Characterize bagging in machine learning, specifically for random forest models -Distinguish boosting in machine learning, specifically for XGBoost models -Explain tuning model parameters and how they affect performance and evaluation metrics
Building deterministic MCP Agents
Learn to build deterministic AI agents using the Model Context Protocol (MCP) and structured quality metrics for repeatable, verifiable outputs. You will explore PMAT as a quality assessment tool for software projects, applying lean manufacturing principles from the Toyota Way including continuous improvement and waste elimination to software quality engineering. The course covers the certainty-scope tradeoff for balancing test coverage and confidence, finite state machine models for deterministic agent behavior, and MCP protocol architecture for structured agent-tool communication. You will analyze survivorship bias in programming language popularity rankings and apply six essential quality metrics for comprehensive project assessment and automated scoring. The testing module covers six essential test types for agent validation, property-based testing for verifying behavioral invariants, and fuzz testing for discovering edge cases using agentic AI. You will use Claude Code as an MCP client integrated with PMAT for automated quality analysis and walk through real-world project examples demonstrating quality scoring across multiple codebases. By completing this course, you will be able to design deterministic agent systems using MCP, apply comprehensive quality metrics with PMAT, and implement property and fuzz testing strategies for robust agent validation.
ArcGIS Desktop for Spatial Analysis: Go from Basic to Pro
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 spatial data analysis with our extensive ArcGIS Desktop course. Begin with an introduction to the core concepts and functionalities of ArcGIS Desktop, ensuring a solid foundation for understanding spatial data. As you progress, you'll explore various methods to access, display, and manipulate spatial data, including raster and vector data formats. You'll be equipped to perform comprehensive spatial analyses by mastering these techniques. The course then transitions to more advanced geoprocessing tasks, where you'll learn about spatial statistics and how to automate processes using Model Builder. These skills are essential for efficiently managing and analyzing large datasets. You'll also gain proficiency in creating informative and visually appealing maps, using base maps, and presenting spatial data effectively. Each module is designed to build upon the last, ensuring a seamless learning experience that enhances your GIS capabilities. Throughout the course, you'll engage with practical examples and hands-on exercises, solidifying your understanding of theoretical concepts through real-world applications. By the end, you'll be proficient in using ArcGIS Desktop for a wide range of spatial analysis tasks, ready to apply these skills in various professional settings. Whether you're new to GIS or looking to enhance your existing skills, this course offers the comprehensive training you need to succeed. This course is designed for GIS professionals, environmental scientists, urban planners, and anyone interested in spatial data analysis. No prior experience with ArcGIS Desktop is required, but a basic understanding of geography and data analysis concepts will be beneficial.
GenAI for DevOps Practitioners
As part of the GenAI Academy, "GenAI for DevOps Practitioners" is an exploration of how Generative Artificial Intelligence (GenAI) is transforming the field of DevOps. This course is a primer where learners will discover the key capabilities of GenAI and uncover practical strategies to leverage these powerful tools in their day-to-day DevOps work. Through a combination of discussions, video demos, and guided hands-on activities, learners will gain an understanding of how GenAI can enhance productivity for code generation, infrastructure as code (IaC), continuous integration/continuous deployment (CI/CD) pipeline optimization, and automated documentation. This course is designed for team leads, managers, and DevOps engineers aiming to enhance efficiency and innovation by integrating GenAI tools into their workflows. It's also ideal for aspiring DevOps practitioners who want to future-proof their skills and gain a competitive edge by mastering GenAI in DevOps. Participants should have a solid grasp of DevOps fundamentals like CI/CD, infrastructure as code, and automation. Familiarity with tools like Git, Jenkins, Docker, Kubernetes, and experience in Python or Shell scripting is essential. An open, curious mindset towards exploring GenAI will be crucial for success. By the end of the course, learners will discover the capabilities of GenAI in enhancing code generation, infrastructure as code, and CI/CD pipeline optimization. They will apply GenAI techniques to real-world DevOps tasks, gaining hands-on experience and evaluating its impact on productivity. Additionally, learners will consider the ethical implications of GenAI, developing strategies for responsible integration while maintaining human oversight and accountability in their DevOps practices.
Building, Optimizing, and Validating Machine Learning Models
Machine learning models rarely perform well without careful design, evaluation, and optimization. In this course, you'll learn how to build machine learning models and systematically improve their performance using proven engineering practices. You’ll start by learning how to map business problems to appropriate machine learning tasks and train multiple model types using common ML libraries. You’ll explore how different algorithms behave under varying data conditions and learn how to justify model choices based on performance and bias-variance trade-offs. Next, you’ll optimize models through systematic hyperparameter tuning and evaluate the computational cost of different algorithms to choose efficient solutions. You’ll also learn validation techniques such as cross-validation and stratified sampling to estimate model performance reliably. The course concludes by showing how to automate machine learning workflows. You’ll build end-to-end pipelines that streamline feature engineering, model training, and optimization so experiments can be reproduced and improved efficiently. By the end of this course, you’ll understand how to design, optimize, and validate machine learning models that are ready for integration into larger ML systems.
Blend Hybrid Search
In the world of AI-powered search, relevance is everything. Go beyond the limits of pure keyword or vector search in Blend Hybrid Search, an intermediate course for developers and ML engineers. You will learn to build a state-of-the-art search system by combining the precision of keyword matching (like BM25) with the semantic power of dense vectors. This course provides a complete, hands-on framework for optimizing search performance using open-source tools as our implementation example. You won't just build a hybrid search function; you will master the art of tuning it. Through a project-driven approach, you will learn to systematically adjust weighting parameters and use the industry-standard NDCG metric to objectively measure and prove the impact of your changes. You will leave with a reusable evaluation script and a data-driven methodology for squeezing the maximum relevance from any search application.
Moneyball and Beyond
The book Moneyball triggered a revolution in the analysis of performance statistics in professional sports, by showing that data analytics could be used to increase team winning percentage. This course shows how to program data using Python to test the claims that lie behind the Moneyball story, and to examine the evolution of Moneyball statistics since the book was published. The learner is led through the process of calculating baseball performance statistics from publicly available datasets. The course progresses from the analysis of on base percentage and slugging percentage to more advanced measures derived using the run expectancy matrix, such as wins above replacement (WAR). By the end of this course the learner will be able to use these statistics to conduct their own team and player analyses.
BigQuery Machine Learning using Soccer Data
This is a self-paced lab that takes place in the Google Cloud console. Learn how to use BigQuery ML with soccer shot data to create and use an expected goals model.
Algoritmos de negociación basados en machine learning
Este curso brinda una introducción a los mercados de capital, la formación de precios, el retorno, la volatilidad, los principios del análisis técnico de activos financieros, algoritmos de negociación basados en modelos de clasificación de machine learning, y sus aplicaciones a estrategias de inversión activas de corto plazo. Este curso está dirigido a personas interesadas en soportar la toma de decisiones de inversión de activos financieros en el mercado de capitales basados en herramientas de analítica. Este curso no requiere de la instalación de ningún programa externo en un equipo local. Todas las herramientas digitales son provistas por la plataforma.
Mastering Tabular ML: Feature Engineering to Production
Master the art and science of building high-performance models for tabular data—the most common data format in industry—through one cohesive, real-world project: a Dynamic Pricing Engine for a ride-hailing platform that predicts trip fares and surge multipliers. You'll start with rigorous EDA and leakage-proof validation, then engineer 150+ high-signal features from numerical, categorical, datetime, and geospatial columns, including target encoding, Haversine distances, and automated feature synthesis with Featuretools. From there, you'll go deep into the engines that dominate tabular ML: master XGBoost internals (regularization, sparsity-aware splits, monotonic constraints) and LightGBM internals (leaf-wise growth, GOSS, EFB, native categorical handling, GPU training), then benchmark them head-to-head alongside CatBoost. Finally, you'll tune with Optuna, select features with SHAP and Boruta, build multi-layer stacking ensembles, and deploy the pricing engine as a production FastAPI endpoint. Following the Kaggle Grandmasters' playbook—large-scale feature generation, stacking, and adversarial validation—you'll finish with a production-ready, portfolio-grade pricing system across 4 modules and 36 focused videos. Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.
Generative AI Part 1
Introduces the theoretical foundations and advanced concepts of neural networks, generative models, transformers, and large language models. Students will explore how these AI systems create new data, process information, and learn through feedback, while analyzing their applications across various fields. The course emphasizes key principles in model building, optimization, and real-world generative AI use cases.
BiteSize Python for Absolute Beginners: Data Structures
This course provides an in-depth exploration of Python’s four built-in data structures: lists, tuples, sets, and dictionaries. Each structure will be introduced in detail, focusing on how to create, access, and manipulate them efficiently. The course will emphasize their unique characteristics and appropriate use cases. Learners will also apply their understanding in a case study, showcasing the practical application of these data structures to solve real-world problems.
人工智慧：機器學習與理論基礎 (Artificial Intelligence - Learning & Theory)
本課程第二部分著重在和人工智慧密不可分的機器學習。課程內容包含了機器學習基礎理論（包含 1990 年代發展的VC理論）、分類器（包含決策樹及支援向量機）、神經網路（包含深度學習）及增強式學習（包含深度增強式學習。 此部份技術包含最早追溯至 1950 年代直到最近 2016 年附近的最新發展。此課程從基礎理論開始，簡介了各機器學習主流技法以及從淺層學習架構演變到最近深度架構的轉換。 本課程之核心目標為： （一）使同學對人工智慧相關的機器學習技術有基礎概念 （二）同學能夠理解機器學習基礎理論、分類器、神經網路、增強式學習 （三）同學能將相關技術應用到自己的問題上 修課前，基礎背景知識： 需要的先備知識：計算機概論 建議的先備知識：資料結構與演算法
Data Analysis with Python
Analyzing data with Python is a key skill for aspiring Data Scientists and Analysts! This course takes you from the basics of importing and cleaning data to building and evaluating predictive models. You’ll learn how to collect data from various sources, wrangle and format it, perform exploratory data analysis (EDA), and create effective visualizations. As you progress, you’ll build linear, multiple, and polynomial regression models, construct data pipelines, and refine your models for better accuracy. Through hands-on labs and projects, you’ll gain practical experience using popular Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, SciPy, and Scikit-learn. These tools will help you manipulate data, create insights, and make predictions. By completing this course, you’ll not only develop strong data analysis skills but also earn a Coursera certificate and an IBM digital badge to showcase your achievement.
Azure AI & ML: Optimize Language Models for AI Applications
This course is designed to provide a comprehensive foundation in Azure Machine Learning, equipping learners with the skills to deploy, manage, and optimize ML models efficiently. Participants will begin by exploring model deployment and consumption in Azure ML, understanding how to operationalize machine learning solutions in production environments. The course progresses to managing and evaluating models, covering key concepts such as performance monitoring, retraining strategies, and best practices for ensuring model accuracy. Learners will gain expertise in Azure AutoML workflows, from data preparation to model selection and evaluation, ensuring automated yet effective ML development. Additionally, the course covers key aspects of MLOps, enabling seamless integration with Azure services for scalable and secure machine learning operations. This course is structured into multiple modules, each featuring lessons and video lectures that provide theoretical insights and hands-on practice. Participants will engage with approximately 3:00–4:00 hours of instructional content, ensuring both conceptual understanding and practical application. To reinforce learning, graded and ungraded assignments are included within each module to test the ability of learners in real-world scenarios. Module 1: Azure AI Foundry: End-to-End Model Development & Optimization Module 2: Optimize model training with Azure Machine Learning By end of this course, you will be able to learn Understand the concepts of Azure AI Foundry, including its role in model optimization, fine-tuning, and retrieval-augmented generation (RAG) strategies. Learn how to explore and manage the Model Catalog and Collections within Azure AI Foundry and ML, and use compute resources effectively. Gain practical experience testing and manually evaluating prompts in the Azure AI Foundry portal playground, including tracking prompt variants. Discover how to create and configure search indexes in the Azure portal, using Azure AI Search for enhanced data retrieval and model deployment.
Fine-tuning Image Models with Diffusion
The Fine-Tuning Image Models with Diffusion course is designed for developers, engineers, and technical product builders who are new to Generative AI but already have intermediate machine learning knowledge, basic Python proficiency, and familiarity with development environments such as VS Code, and who want to engineer, customize, and deploy open generative AI solutions while avoiding vendor lock-in. The course gives learners hands-on experience adapting generative image models for custom styles and applications. The course begins with the foundations of diffusion models, explaining forward and reverse diffusion processes and exploring the key components of Stable Diffusion architectures, including U-Net, VAE, and text encoders. Learners then apply Low-Rank Adaptation (LoRA) techniques to train efficiently on consumer hardware, comparing performance and trade-offs with full fine-tuning. In the second module, learners implement DreamBooth, a methodology for training on limited datasets to personalize models with custom concepts and artistic styles. Learners practice dataset preparation, hyperparameter tuning, and checkpoint management while preserving model generalization. The third module introduces ComfyUI, where learners design and execute node-based workflows that integrate fine-tuned models with advanced extensions like ControlNet. And, in the final module, learners will optimize fine-tuned diffusion models for production by systematically adjusting inference parameters to achieve optimal trade-offs between image quality, generation speed, and resource efficiency. By the end of the course, learners will have produced a custom fine-tuned diffusion model, integrated it into ComfyUI pipelines, and optimized it for production-quality image generation.
Statistics and Calculus Methods for Data Analysis
This program focuses on the practical application of essential mathematical, statistical, and analytical techniques vital for advanced data science studies. Learn to calculate expected values, understand the normal distribution, perform derivative calculations, and solve complex integrals, all demonstrated with Python. Start with the concept of expected values and explore their relationship to the normal distribution, laying the groundwork for statistical analysis and predictive modeling. Move on to calculus, mastering derivatives and their applications in tasks like optimization and rate of change analysis. Advance further into solving integrals, including techniques for handling complex integrations and their significance in continuous data analysis. By the end of the course, you will possess a strong mathematical foundation to tackle more advanced data science topics. Engage in practical assignments and real-world projects to apply these methods in solving complex data problems. By leveraging tools like Python, you will gain hands-on understanding of these critical concepts.
Govern Your GenAI Data Safely
The explosion of generative AI has created unprecedented data governance challenges that traditional approaches can't handle. This course equips you with the specialized skills to govern GenAI data safely while maintaining operational agility. This Short Course was created to help machine learning and AI professionals accomplish secure, compliant GenAI data governance at enterprise scale. By completing this course, you'll be able to design sophisticated role-based access control systems, assess your organization's governance maturity using industry frameworks like DAMA-DMBOK, and create comprehensive stewardship programs that balance innovation with security. These are the foundational skills that separate GenAI operations that scale safely from those that create compliance nightmares. By the end of this course, you will be able to: - Analyze data access patterns across user cohorts to recommend precise role-based controls - Evaluate governance maturity using established frameworks to identify strategic improvement opportunities - Create data stewardship programs with clear ownership, quality standards, and governance procedures This course is unique because it bridges the gap between cutting-edge GenAI capabilities and enterprise-grade governance, focusing specifically on the intersection of AI operations and data security. To be successful in this project, you should have experience with data analytics, understanding of enterprise risk concepts, and familiarity with AI/ML environments.
Python: Implement & Evaluate Random Forests for ML
Build practical machine learning skills by implementing and evaluating Random Forest models in Python. In this hands-on course, you'll work through a complete supervised learning workflow using the SONAR dataset, from data preparation and exploration to decision tree construction and Random Forest model evaluation. Through guided coding exercises, you'll learn how to load and inspect data, apply decision tree splitting techniques using the Gini index, and evaluate classification performance with cross-validation. You'll then assemble a Random Forest classifier and assess its effectiveness using structured validation approaches and performance analysis. This course is designed for learners with a basic understanding of Python who want to strengthen their knowledge of supervised machine learning through practical, code-based learning. Rather than focusing only on theory, you'll implement each step of the modeling process and evaluate model performance using established validation techniques. By the end of the course, you'll be able to build and evaluate Random Forest classifiers in Python, apply data preparation techniques, use impurity measures to construct decision trees, and assess classification models with confidence. If you're looking for a practical introduction to Random Forests and supervised learning, this course provides a structured, project-based learning experience.
Apply AI Techniques & Prescriptives
Transform your analytical capabilities into competitive advantage with AI-powered decision intelligence. This Short Course was created to help data analysts accomplish strategic business impact through advanced AI techniques and prescriptive analytics. By completing this course, you'll be able to build ensemble AI solutions that combine multiple methodologies, evaluate performance trade-offs across competing models, and implement optimization frameworks that drive measurable business outcomes. By the end of this course, you will be able to: Apply ensemble AI techniques to solve defined business problems with documented rationale Evaluate accuracy, latency, and interpretability trade-offs across multiple AI approaches Implement linear programming optimization for product mix and profit maximization Create weighted-scoring models for prescriptive scenario evaluation This course is unique because it bridges the gap between technical AI implementation and strategic business decision-making, providing hands-on experience with real-world optimization challenges. To be successful in this project, you should have a background in basic analytics, Python programming, and business problem-solving experience.
No-Code Model Evaluation, Communication, and Business Impact
Develop a thorough understanding of model evaluation beyond accuracy—a vital skill in professional business analytics. Test models against constantly changing data, compare competing solutions, and adjust strategies for maximum reliability and business fit.