AI/ML Engineering Textbook

A comprehensive guide to Artificial Intelligence and Machine Learning, from mathematical foundations to building agentic systems.

Parts

Chapters

271+

Sections

63+

Libraries

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Part 1

Mathematical Foundations for Machine Learning

Essential mathematical concepts including linear algebra, calculus, probability, and statistics that form the foundation of machine learning algorithms.

3 Chapters

Part 2

Python Ecosystem and Classical Machine Learning

Master the Python data science stack and classical machine learning algorithms including supervised and unsupervised learning.

5 Chapters

Part 3

Deep Learning Fundamentals

Build neural networks from scratch, understand backpropagation, and learn to train deep networks effectively.

4 Chapters

Part 4

Modern Architectures and Large Language Models

Explore transformer architecture, pre-trained language models, fine-tuning techniques, and efficient inference.

5 Chapters

Part 5

Generative Models

Understand and implement VAEs, GANs, and diffusion models for generating images and other content.

3 Chapters

Part 6

Multimodal AI and Vision-Language Models

Learn about vision-language models, CLIP, and text-to-image systems like Stable Diffusion.

2 Chapters

Part 7

Applied AI Systems

Build production-ready AI systems including RAG, agents, reinforcement learning, and MLOps pipelines.

5 Chapters

Part 8

Data Science: Process & Practice

The end-to-end data science workflow: framing problems, acquiring and wrangling data, exploring it, engineering features, and communicating results.

5 Chapters

Part 9

Statistics & Statistical Inference

From descriptive statistics and probability distributions to hypothesis testing, the bootstrap, Bayesian inference, regression, and experimental design.

8 Chapters

Part 10