Machine Learning

Available resources online.

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Online Books.

Classical Machine Learning

• A Brief Introduction to Machine Learning for Engineers. Osvaldo Simeone.
• A Comprehensive Guide to Machine Learning. Soroush Nasiriany, Garrett Thomas, William Wang, Alex Yang, UC Berkeley.
• A course in Machine Learning. Hal Daumé III.
• Algorithms for Decision Making. Mykel J. Kochenderfer, Tim A. Wheeler,and Kyle H. Wra. MIT Press.
• Approaching (almost) any Machine Learning Problem. Abhishek Thakur.
• Bayesian Modeling and Computation in Python. Martin Osvaldo A, Kumar Ravin; Lao Junpeng, 2021.
• Big Data and AI Strategies. Marco Kolanovic, Rajesh T. Krishnamachari. JPMorgan Chase & Co.
• Cluster Analysis, 5th. Edition. Brian S. Everitt, Sabine Landau, Morven Leese, and Daniel Stahl
• CS229: Machine Learning. Andrew Ng, Tengyu Ma. Stanford University.
• Concise Machine Learning. Jonathan Richard Shewchuk, UC Berkeley.
• Data Clustering. Algorithms and Applications. Charu C. Aggarwal and Chandan K. Reddy Editors.
• Foundations of Machine Learning. 2nd. ed. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar.
• Hands-on in Machine Learning with Scikit-Learn, Keras & Tensorflow, 3rd. Ed., 2nd. Ed. (ML Notebooks). Aurélien Géron.
• Interpretable Machine Learning. A Guide for Making Black Box Models Explainable. Christoph Molnar.
• Introduction to Machine Learning. Alex Smola and S.V.N. Vishwanathan.
• Introduction to Machine Learning with Python. Andreas Muller and Sarah Guido.
• Intuitive Machine Learning. Vincent Granville.
• Machine Learning Engineering. Andriy Burkov.
• Machine Learning from Scratch. Derivations in Concepts and Code. Danny Friedman.
• Machine Learning in Action.Peter Harrington.
• Machine Learning with PyTorch and Scikit-Learn Book. Sebastian Raschka, Yuxi (Hayden) Liu, and Vahid Mirjalili.
• Machine Learning with TensorFlow.js. Annan Hashmi.
• Machine Learning Yearning. Andrew Ng.
• Mathematics for Machine Learning. Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong.
• Mathematics for Machine Learning. Garret Thomas. UC Berkeley.
• Mining of Massive Data. Jure Leskovec, Anand Rajaraman, Jeff Ullman.
• Model-based Machine Learning. John Winn with Christopher M. Bishop, Thomas Diethe, John Guiver, and Yordan Zaykov.
• Network Science. Albert-László Barabási.
• Notes on Data Science & Machine Learning. Chris Albon.
• Pattern Recognition and Machine Learning. Christopher Bishop.
• Patterns, Predictions, and Actions. A History about Machine Learning. Morris Hardt and Benjamin Recht.
• Practical Machine Learning with PyTorch. Atkinson, & Denholm. Journal of Open Source Education, 7(76), 239. |Code.
• Probabilistic Machine Learning: An Introduction. Kevin P. Murphy.
• Probabilistic Machine Learning: Advanced Topics. Kevin P. Murphy.
• Python Data Science Handbook. Jake VanderPlas.
• Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn and Tensorflow 2. Sebastian Raschka and Vahid Mirkalili.
• Python Machine Learning Book Code Repository. Sebastian Raschka.
• Python Machine Learning By Example 3rd. Ed., Book Code Repository. Yuxi (Hayden) Liu.
• Reinforcement Learning: An Introduction. 2nd. ed. Richard S. Sutton and Andrew G. Barto.
• Rules of Machine Learning: Best Practices for ML Engineering. Martin Zinkevich.
• Scrapbook. Unorganized Notes. Stephan Osterburg.
• Speech and Language Processing (3rd ed. draft). Dan Jurafsky and James H. Martin.
• Statistics and Machine Learning in Python. Edouard Duchesnay, Tommy Löfstedt, Feki Younes.
• Text Mining with R. Julia Silge and David Robinson.
• The Elements of Statistical Learning. Data Mining, Inference, and Prediction. Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
• The Hundred-Page Machine Learning Book. Andriy Burkov.
• The Illustrated Machine Learning Website. Francesco di Salvo, Mateo Bernabito, Simone Raponi.
• Understanding Machine Learning: From Theory to Algorithms. Shai Shalev-Shwartz and Shai Ben-David.
• Unraveling Principal Component Analysis. Peter Bloem.

Deep Learning

• Alice’s Adventures in a differentiable wonderland. A Primer on designing neural networks. Simone Scardapane.
• Deep Learning. I. Goodfellow, Y. Bengio and A. Courville.
• Deep Learning. Foundations and Concepts. Christopher M. Bishop, Hugh Bishop.
• Deep Learning for Natural Language Processing: A Gentle Introduction. Mihai Surdeanu and Marco A. Valenzuela-Escárcega.
• Dive into Deep Learning. Interactive deep learning book with code, math, and discussions. Various authors.
• Introduction to Reinforcement Learning. Richard S. Sutton, Andrew G. Barto.
• Machine and Deep Learning Compendium. Ori Cohen.
• Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory. Arnulf Jentzen, Benno Kuckuck and Philippe von Wurstemberger.
• Mathematical theory of deep learning. Philipp Petersen, Jakob Zech. arXiv:2407.18384.
• ML Papers Explained. DAIR-AI, Democratizing Artificial Intelligence Research, Education, and Technologies.
• Natural Language Processing. Jacob Einsenstein.
• Natural Language Processing with Python. Steven Bird, Ewan Klein, and Edward Loper.
• Neural Networks and Deep Learning. Michael Nielsen.
• Papers with Code: free and open resource with Machine Learning papers, code, datasets, methods and evaluation tables.
• Physics-based Deep Learning. N. Thuerey, P. Holl, M. Mueller, P. Schnell, F. Trost, K. Um.
• Programming PyTorch for Deep Learning. Creating and Deploying Deep Learning Applications. Ian Pointer.
• Reinforcement Learning. An introduction. 2nd. Ed. ( Lisp code | Python code | References.). Richard S. Sutton, Andrew G. Barto.
• SamGeo. Segment Anything Model for geospatial data. Wu, Q., Osco, L.
• Speech and Language Processing. An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models. 3rd. Ed. Daniel Jurafsky, James H. Martin.
• The Engineer's Guide to Deep Learning. Hironobu Suzuki.
• The Little Book of Deep Learning. François Fleuret.
• The Principles of Deep Learning Theory. Daniel A. Roberts, Sho Yaida, Boris Hanin. Cambridge University Press.
• Transformers for Natural Language Processing. 2nd. Ed. Denis Rothman.
• Understanding Deep Learning. Simon J.D. Prince.

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Open Courses

• AI Watchlist. Distilled AI.
• Applications of Deep Neural Networks. T81 558, Jeff Heaton. Washington University in Saint Louis.
• Applied Machine Learning. 2019 Fall CS5785 Cornell Tech.
• Distilled AI. Courses, Primers, Research, Coding, Lists.
• Full Stack Machine Learning. Full Stack Deep Learning.
• Introduction to Deep Learning. Yann LeCun, NYU.
• Google Machine Learning Education. Foundational courses by Google.
• Introduction to Machine Learning. Simpl¡Learn.com.
• Reinforcement Learning Course. David Silver.
• MIT 6.S191: Introduction to Deep Learning. Alexander Amini.

Microsoft AI lessons

• Data Science for beginners
• AI for beginners
• Machine Learning for beginners
• Generative AI for beginners

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PyTorch

• PyTorch Tutorials.
• Deep Learning Fundamentals (PyTorch Lightning). Sebastian Raschka.
• Programming PyTorch for Deep Learning. Ian Pointer.

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Jupyter Notebooks Examples.

• Scikit-Learn Tutorial. Jake VanderPlas.

Other

• Machine Learning Glossary
• Machine Learning Glossary
• Awesome Machine Learning Visualizations (in Github). Mohamed Kedir Noordeen.
• Cheat Sheets for Machine Learning and Data Science. Aqeel Anwar.
• ConvNetJS: Deep Learning in your browser
• Mathematics for Machine Learning - dependency structure of knowledge.

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Updated: 02/19/2025 (C. Lizarraga)