This is a repository containing resources for learning Data Science and Machine Learning. Our aim is to collect in one single place high-quality resouces and learning materials to help you master this subject. Most of the resources are from top university all around the world, we tried collecting best materials and to avoid a large list of unstructured resources we hand-picked the courses which we consider the best in each subject, and structured this repository in a manner that is easy to navigate and find you want.
- Gilbert Strang, Introduction to Linear Algebra, Sixth Edition, 2023:
- Gilbert Strang, Linear Algebra for Everyone, 2020: https://math.mit.edu/~gs/everyone/
- Gilbert Strang, Linear Algebra for Everyone, Solutions Manual. Available online::
- Gilbert Strang, Linear Algebra and Learning from Data, 2019. Available online:
- Gilbert Strang, Linear Algebra and Learning from Data, Solutions Manual. Available online:
- Robert van de Geijn and Maggie Myers, Advanced Linear Algebra: Foundations to Frontiers, December 2022, Available online:
- MIT 18.06 (Freely available on OCW):
- Linear Algebra: Foundations to Frontiers:
- http://www.ulaff.net/
- Also available via Edx: https://www.edx.org/course/linear-algebra-foundations-to-frontiers
- Khan Academy - Linear Algebra:
- Georgia Tech - Introductory Linear Algebra:
- Georgia Tech - Applications of Linear Algebra:
- Rice University Part 1 - Introduction to Linear Algebra:
- 3blue1brown - Introduction to Linear Algebra:
- Imperial College London - Mathematics for Machine Learning Linear Algebra:
- Mark Embree, Matrix Methods for Computational Modeling and Data Analytics, Virginia Tech, 2023:
- Gilbert Strang & Edwin Herman, Calculus
- Gilbert Strang & Edwin Herman, Calculus Vol. 1: https://openstax.org/details/books/calculus-volume-1
- Gilbert Strang & Edwin Herman, Calculus Vol. 2: https://openstax.org/details/books/calculus-volume-2
- Gilbert Strang & Edwin Herman, Calculus Vol. 3: https://openstax.org/details/books/calculus-volume-3
- James Stewart, Calculus: Early Transcendentals (Latest: Ninth Edition).
- MIT 18.01
- And available via MIT OpenLearningLibrary: https://openlearninglibrary.mit.edu/courses/course-v1:MITx+18.01.1x+2T2019/course/
- Also available via Edx: https://www.edx.org/xseries/mitx-18.01x-single-variable-calculus
- Harvard - Calculus Applied:
- Khan Academy - Calculus:
- University of Sydney - Introduction to Calculus:
- Dimitri P. Bertsekas and John N. Tsitsiklis, Problem solutions for "Introduction to Probability" (2nd Edition):
- Larry Wasserman, All of Statistics, Updated 2003.
- Introduction to Modern Statistics:
- Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin, Bayesian Data Analysis, 3rd edition. Available online (with typos fixed!):
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- James E. Gentle, Theory of Statistics, Updated 2025.
- Professor Rigollet's lecture notes for the course on High-Dimensional Statistics, in a single PDF, Updated 2023:
- Rafael Irizarry, Introduction to Data Science. Available online:
- Bishop, Pattern Recognition and Machine Learning.
- Brunton & Kutz, Data-Driven Science and Engineering.
- Russell & Norvig, Artificial Intelligence A Modern Approach.
- Steven L. Brunton, J. Nathan Kutz, Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. 2nd Edition. Cambridge University Press. 2022.
- Novak, Numerical Methods for Scientific Computing - Second Edition, 2022. Available online:
- Lehman, E., F.T. Leighton, and A.R. Meyer, Mathematics for Computer Science, 2015.
- Michael Pyrcz, Data Analytics, Geostatistics and Machine Learning, Interactive dashboards, The University of Texas at Austin.
- James H. Stock and Mark W. Watson, Introduction to Econometrics, 4th Edition, Pearson.
- Jeffrey M. Wooldridge, Introductory Econometrics: A Modern Approach, 7th Edition, Cengage Learning.
- Abhijit V. Banerjee and Esther Duflo, Good Economics for Hard Times, MIT Press, 2021.
- Abhijit V. Banerjee and Esther Duflo, Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty, MIT Press, 2011.
- Joshua D. Angrist and Jörn-Steffen Pischke, Mastering 'Metrics: The Path from Cause to Effect, Princeton University Press, 2014.
- Joshua D. Angrist and Jörn-Steffen Pischke, Mostly Harmless Econometrics: An Empiricist's Companion, Princeton University Press, 2008.
- Rasmussen & Williams, Gaussian Processes for Machine Learning.
- Lopez & Lopez & Crossa, Multivariate Statistical Machine Learning Methods for Genomic Prediction.
- Mohri & Rostamizadeh & Talwalkar, Foundations of Machine Learning.
- Kevin Patrick Murphy, Probabilistic Machine Learning: An Introduction, MIT Press, 2022.
- Link to the free textbook and other resources: https://probml.github.io/pml-book/book1.html
- Solution manual for selected problems: https://probml.github.io/pml-book/solns-public.pdf
- The Github repo with notebooks: https://probml.github.io/pml-book/book1.html
- Ethem Alpaydın, Introduction to Machine Learning, MIT Press (4th edition, 2020).
- Shai Shalev-Shwartz and Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014. https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/
- Avrim Blum, John Hopcroft, and Ravindran Kannan, Foundations of Data Science, Cambridge University Press, 2020.
- Free version (dated 2018): https://www.cs.cornell.edu/jeh/book.pdf
- Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, Mathematics for Machine Learning, Cambridge University Press, 2020. Available free: https://mml-book.github.io/
- Stanford - CS 229 ― Machine Learning:
- Justin Solomon, Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics, A K Peters/CRC Press, 2015.
- Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Machine Learning: Fundamental Concepts and Algorithms, 2nd Edition, Cambridge University Press, March 2020. ISBN: 978-1108473989.
- Caltech - Learning From Data:
- Andrew Ng - Machine Learning Specialization:
- MIT - MIT 6.034 Artificial Intelligence:
- Goodfellow & Bengio & Courville, Deep Learning:
- Michael Nielsen, Neural Networks and Deep Learning:
- Hastie & Tibshirani & Friedman - The Elements of Statistical Learning Data Mining Inference and Prediction, made available here by one of the authors:
- Jurafsky & Martin - Speech and Language Processing: