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
- How to deploy a machine learning model into production
- Data engineering fundamentals with Kafka and Spark architecture and why data engineering is critical in AI projects
- 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
- MCP and how to build a local MCP server and client
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.
