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Full program

The ideal AI path

From zero to AI engineer. Follow the modules in order: each phase builds on the previous one to take you, step by step, to complete mastery.

42modules

9phases

250 h+of content

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Phase 1

Foundations

A solid base: AI culture, Python and mathematics.

1Introduction to AI — Complete 2Python for AI — Complete 3Mathematics for AI

Phase 2

Machine Learning

Learn from data: models, evaluation and features.

4Supervised Learning 5Unsupervised Learning 6Feature Engineering

Phase 3

Deep Learning

Neural networks, from perceptron to transformers.

7Deep Learning Fundamentals 8TensorFlow & Keras 9PyTorch 10Convolutional Neural Networks 11RNN & LSTM 12Transformers & Attention

Phase 4

NLP & LLM

Language: from tokenization to production LLMs.

13Natural Language Processing 14Large Language Models 15Prompt Engineering 16RAG Systems 17Model Fine-tuning 18Small Language Models

Phase 5

Vision & Generative AI

See and create: images, video and generation.

19Advanced Computer Vision 20Generative AI 21Stable Diffusion

Phase 6

Advanced ML

Reinforcement, scale and specialized models.

22Reinforcement Learning 23Apache Spark ML 24Time Series Forecasting 25Recommender Systems 26Elasticsearch, Kibana & Neo4j

Phase 7

MLOps & Cloud

From notebook to reliable production systems.

27MLOps — Complete 28Feature Stores 29AWS SageMaker 30Azure Machine Learning 31GCP Vertex AI

Phase 8

Agents & Local AI

Autonomous agents, tools and self-hosted models.

32LangChain 33Ollama — Local LLMs 34AI Agents & AutoGPT 35CrewAI — Multi-Agents 36Claude Code & Slash Commands

Phase 9

Deployment & Responsibility

Ship your models everywhere — ethically.

37TensorFlow Lite 38ONNX Runtime 39Streamlit 40Gradio 41FastAPI for ML 42AI Ethics & Responsible AI

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