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Textbook Content
Part 1Mathematical Foundations for Machine Learning
Part 2Python Ecosystem and Classical Machine Learning
Part 3Deep Learning Fundamentals
Part 4Modern Architectures and Large Language Models
Part 5Generative Models
Part 6Multimodal AI and Vision-Language Models
- Ch 21: Vision-Language Foundations
- Ch 22: Text-to-Image and Beyond
Part 7Applied AI Systems
Part 8Data Science: Process & Practice
Part 9Statistics & Statistical Inference
Part 10Databases & SQL for Data Science
Part 11Time Series Analysis & Forecasting
Part 12Programming for Data Science
Appendices
Appendix A: Environment Setup Guide
Appendix B: GPU Computing Fundamentals
Appendix C: Cloud Platform Quick Start
Appendix D: Mathematics Reference
Appendix E: Library Quick Reference
Appendix F: Python / R / SQL Rosetta Stone
Appendix G: Statistics & Probability Formula Reference
Appendix H: SQL Recipes & Patterns
Appendix I: Data Science Interview Preparation
Resources
Library Reference
Learning Paths
Jupyter Notebooks
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A comprehensive guide to AI/ML engineering, from mathematical foundations to agentic systems. Static build.