AI/ML Engineering Textbook

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

Parts

Chapters

136+

Sections

50+

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

Library Reference

Explore all the Python libraries covered in this textbook, from NumPy to PyTorch.

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Learning Paths

Follow curated learning paths based on your goals and experience level.

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AI/ML Engineering Textbook

Table of Contents

Mathematical Foundations for Machine Learning

Python Ecosystem and Classical Machine Learning

Deep Learning Fundamentals

Modern Architectures and Large Language Models

Generative Models

Multimodal AI and Vision-Language Models

Applied AI Systems

Library Reference

Learning Paths