AI & Machine Learning Book References
A curated and structured collection of essential AI & Machine Learning books for practitioners, researchers, and learners at all levels.
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About This Repository
This repository serves as a knowledge hub for Artificial Intelligence, Machine Learning, Deep Learning, Data Science, and related domains.
Each reference is selected to provide:
- Strong theoretical foundations
- Practical, industry-oriented insights
- Production-level engineering perspectives
Whether you are starting out or refining advanced expertise, this collection supports continuous learning across the AI stack.
Book References
| # | Title | Author(s) | Topic Area | Key Focus | Level | Link |
|---|---|---|---|---|---|---|
| 1 | Designing Machine Learning Systems | Chip Huyen | Machine Learning | Production ML systems & iteration | Intermediate | |
| 2 | Python for Data Analysis | Wes McKinney | Data Science | Data analysis with pandas & NumPy | Beginner | |
| 3 | SQL Cookbook | Anthony Molinaro | Databases | Practical SQL solutions | Intermediate | |
| 4 | AI Engineering | Chip Huyen | Artificial Intelligence | Foundation-model-driven systems | Advanced | |
| 5 | Deep Learning from Scratch | Unknown | Deep Learning | Neural networks from fundamentals | Intermediate | |
| 6 | Hands-On Machine Learning | Aurélien Géron | Machine Learning | Practical ML with sklearn & TF | Beginner–Intermediate | |
| 7 | Human-in-the-Loop ML | Unknown | Machine Learning | Active learning workflows | Intermediate | |
| 8 | ML with Python Cookbook | Umeh & Albon | Data Science | End-to-end ML recipes | Intermediate | |
| 9 | Mathematics for ML | Unknown | Mathematics | Linear algebra, probability | Advanced | |
| 10 | Building ML Systems | Unknown | Machine Learning | System architecture & pipelines | Intermediate | |
| 11 | Introduction to Deep Learning | Cao Xiao, Jimeng Sun | Deep Learning | DL foundations | Beginner–Intermediate | |
| 12 | Data Analysis with NumPy & pandas | Unknown | Data Science | Data manipulation | Beginner–Intermediate | |
| 13 | Data Visualization | Unknown | Visualization | Data storytelling | Intermediate | |
| 14 | Data Science for Business | Provost & Fawcett | Data Science | Business decision-making | Intermediate | |
| 15 | Computer Vision | Forsyth & Ponce | Computer Vision | Vision algorithms | Advanced | |
| 16 | Deep Learning Methods | Unknown | Deep Learning | DL techniques | Intermediate | |
| 17 | Deep Learning Applications | Unknown | Deep Learning | Applied DL | Intermediate | |
| 18 | Elements of Statistical Learning | Hastie et al. | Statistics / ML | Statistical learning theory | Advanced | |
| 19 | Exercises in ML | Michael Gutmann | Practice | ML problem sets | Intermediate | |
| 20 | Feature Engineering | Zheng & Casari | Data Prep | Feature design | Intermediate | |
| 21 | Graph Machine Learning | Unknown | Graph ML | GNNs & graph methods | Intermediate | |
| 22 | ML for Absolute Beginners | Unknown | Machine Learning | Intro concepts | Beginner | |
| 23 | Python Machine Learning | Unknown | ML / Python | ML with Python | Beginner–Intermediate | |
| 24 | Quantitative Economics | Sargent & Stachurski | Economics | Econ modeling | Intermediate–Advanced | |
| 25 | The Little Book of DL | François Fleuret | Deep Learning | Concise DL overview | Beginner–Intermediate | |
| 26 | Artificial Intelligence: A Modern Approach | Stuart Russel | Artificial Intelligence | Comprehensive AI foundations | Advanced | |
| 27 | Artificial Intelligence by Example | Denis Rothman | AI/Machine Learning | Advanced AI & DL design skills | Intermediate–Advanced | |
| 28 | Bayesian Reasoning and Machine Learning | David Barber | Machine Learning | Probabilistic ML methods | Advanced | |
| 29 | Hands-on Data Science | Unknown | Data Science | Practical data science workflow | Intermediate | Hands-on-data-science.pdf |
| 30 | Machine Learning Algorithms (2nd Edition) | Unknown | Machine Learning | ML algorithms with color visuals | Intermediate | Coming Soon |
| 31 | Mastering Machine Learning Algorithms | Unknown | Machine Learning | Advanced ML algorithms | Advanced | Coming Soon |
| 32 | OpenCV 4 with Python Blueprints | Unknown | Computer Vision | Advanced CV projects with Python | Intermediate | Coming Soon |
| 33 | AI & Machine Learning for Coders & Programmers | Unknown | AI/Machine Learning | ML implementation for programmers | Intermediate | Coming Soon |
Repository Structure
AI-ML-Book-References/
├── README.md
└── LICENSE
How to Use This Repository
- Browse through the reference table to find books relevant to your learning goals
- Filter by topic area or expertise level to identify appropriate resources
- Each book has been selected for its pedagogical value and industry relevance
- References span from foundational concepts to advanced implementation techniques
Learning Paths
For Beginners
Start with fundamental concepts in Python for Data Analysis and Hands-On Machine Learning to build a solid foundation.
For Intermediate Learners
Explore specialized topics through SQL Cookbook, Machine Learning with Python Cookbook, and Mathematics for Inference and Machine Learning.
For Advanced Practitioners
Deepen expertise with Deep Learning from Scratch, AI Engineering, and Designing Machine Learning Systems.
Contributing
Recommendations for new book references are welcome. Please ensure suggested materials:
- Provide substantial value to AI/ML practitioners
- Maintain current relevance in the field
- Contribute unique perspectives or expertise
License
This repository is licensed under the MIT License. See the LICENSE file for details.
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