The Complete Full Stack AI Engineering Bootcamp

Descripción

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.

Lo que aprenderás

• Build end to end AI Engineering projects using Python, PyTorch, scikit-learn, and SQL from data processing to model deployment.
• Master Natural Language Processing (NLP) and Transformers by implementing real projects with Hugging Face, BERT, T5, and Large Language Models (LLMs).
• Develop production ready AI APIs using FastAPI and Docker for scalable model deployment.
• Understand and implement LangChain and LangGraph to build multi agent LLM applications with memory, tools, and workflows.
• Learn Model Context Protocol (MCP) and create MCP servers and clients for advanced AI tool integration.
• Perform data analysis, visualization, and feature engineering using Matplotlib and scikit-learn for machine learning pipelines.
• Design AI systems with context engineering, prompt engineering, RAG, and memory management.
• Gain practical skills required for AI Engineer, NLP Engineer, and LLM Engineer roles in the industry.
• Run open source LLMs locally using Ollama
• Integrate Ollama models with Python and LangChain to build local AI applications
• Design scalable data pipelines using Azure Databricks, PySpark, and Delta Lake for real-world AI applications
• Apply MLflow for experiment tracking, model management, and building reproducible AI and LLM workflows

Requisitos

• No prior knowledge of AI or machine learning is required. Everything is taught from the ground up
• A laptop or desktop with internet connection to run Python, VS Code, and required tools
• An OpenAI API key will be required for some LLM and AI application examples (usage may incur small costs)
• Azure Databricks will be used in the data engineering section, and creating/using it may involve cloud usage charges