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π "Mathematical Methods in Data Science with Python" by Sebastian Roche.
π https://mmids-textbook.github.io
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π¨ Cambridge has just released a real bombshell this time.
π A whole collection of classic textbooks on AI and machine learning is now available for free in PDF format.
If you want to really understand machine learning and don't want to waste money on overpriced courses, these ten books will be enough to build a very solid foundation.
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1οΈβ£ Understanding Machine Learning
One of the best books for beginners. It covers the basic theoretical algorithms of machine learning.
π https://cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf
2οΈβ£ Mathematical Foundations of Machine Learning
If you're not very confident in your math skills, I would start here.
π https://mml-book.github.io/book/mml-book.pdf
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A more in-depth look at the mathematical principles of machine learning algorithms.
π https://tongzhang-ml.org/lt-book/lt-book.pdf
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The theoretical foundations of deep learning and an understanding of why it all works.
π https://arxiv.org/pdf/2106.10165
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A systematic analysis of neural networks and the principles of their training.
π https://arxiv.org/pdf/1901.05639
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A good starting point for those who want to understand graph neural networks.
π https://yaoma24.github.io/dlg_book/dlg_book.pdf
7οΈβ£ Machine Learning: A Probabilistic Perspective
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π https://people.csail.mit.edu/moitra/docs/bookexv2.pdf
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Fundamental theory of probability. Very useful if you want to understand machine learning beyond the level of using ready-made libraries.
π https://sites.math.duke.edu/~rtd/PTE/PTE5_011119.pdf
9οΈβ£ Fundamentals of Applied Probability
More focus on the practical application of probability theory.
π https://sites.math.duke.edu/~rtd/EP4A/EP4A_April2021.pdf
π Advanced Data Analysis
An advanced level for those who want to seriously improve their data analysis skills.
π https://stat.cmu.edu/~cshalizi/ADAfaEPoV/ADAfaEPoV.pdf
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π A whole collection of classic textbooks on AI and machine learning is now available for free in PDF format.
If you want to really understand machine learning and don't want to waste money on overpriced courses, these ten books will be enough to build a very solid foundation.
From simple to complex.
1οΈβ£ Understanding Machine Learning
One of the best books for beginners. It covers the basic theoretical algorithms of machine learning.
π https://cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf
2οΈβ£ Mathematical Foundations of Machine Learning
If you're not very confident in your math skills, I would start here.
π https://mml-book.github.io/book/mml-book.pdf
3οΈβ£ Mathematical Analysis of Machine Learning Algorithms
A more in-depth look at the mathematical principles of machine learning algorithms.
π https://tongzhang-ml.org/lt-book/lt-book.pdf
4οΈβ£ Theoretical Principles of Deep Learning
The theoretical foundations of deep learning and an understanding of why it all works.
π https://arxiv.org/pdf/2106.10165
5οΈβ£ Neural Networks and Learning Machines
A systematic analysis of neural networks and the principles of their training.
π https://arxiv.org/pdf/1901.05639
6οΈβ£ Graph Deep Learning
A good starting point for those who want to understand graph neural networks.
π https://yaoma24.github.io/dlg_book/dlg_book.pdf
7οΈβ£ Machine Learning: A Probabilistic Perspective
It allows you to look at machine learning from a probabilistic and algorithmic perspective.
π https://people.csail.mit.edu/moitra/docs/bookexv2.pdf
8οΈβ£ Probability Theory: Theory and Examples
Fundamental theory of probability. Very useful if you want to understand machine learning beyond the level of using ready-made libraries.
π https://sites.math.duke.edu/~rtd/PTE/PTE5_011119.pdf
9οΈβ£ Fundamentals of Applied Probability
More focus on the practical application of probability theory.
π https://sites.math.duke.edu/~rtd/EP4A/EP4A_April2021.pdf
π Advanced Data Analysis
An advanced level for those who want to seriously improve their data analysis skills.
π https://stat.cmu.edu/~cshalizi/ADAfaEPoV/ADAfaEPoV.pdf
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Matrix Calculus for Machine Learning and Beyond! β a free ebook from MIT.
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This is the 2025 MIT textbook by Alan Edelman, Steven G. Johnson, and Paige Bright.
The book directly connects matrix calculus to modern machine learning.
Inside:
* Derivatives of matrices and vectors
* Jacobian and Hessian
* Matrix decompositions
* Optimization
* Differentiation in reverse mode
* Backpropagation of error
* Automatic differentiation
* Derivatives through ODEs
* Problems focused on machine learning
This is a comprehensive mathematical bridge between linear algebra, calculus, optimization, backpropagation, and machine learning.
Free ebook:
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Forwarded from Machine Learning with Python
"Linear Algebra with Applications" is a free and comprehensive textbook that introduces the computational, theoretical, and applied aspects of linear algebra.
The book covers topics such as systems of linear equations, matrices, determinants, vector spaces, linear transformations, eigenvalues and eigenvectors, diagonalization, inner product spaces, orthogonality, and many more. The explanations are accompanied by over 330 worked examples, exercises, and practical applications in geometry, electrical networks, dynamic systems, probability theory, and optimization.
A particularly interesting section discusses how Google's PageRank algorithm uses the dominant eigenvector to rank web pages. The links between websites are represented as a connectivity matrix, and the components of its dominant eigenvector provide an estimate of the relative importance of each page.
This is a very clear example of how an apparently abstract idea from linear algebra can underlie a real-world technology used on a massive scale.
The 2023 edition is available under a Creative Commons license. This is another excellent resource that is worth keeping as a reference.
https://collection.bccampus.ca/textbook/qTj4b4Ey
The book covers topics such as systems of linear equations, matrices, determinants, vector spaces, linear transformations, eigenvalues and eigenvectors, diagonalization, inner product spaces, orthogonality, and many more. The explanations are accompanied by over 330 worked examples, exercises, and practical applications in geometry, electrical networks, dynamic systems, probability theory, and optimization.
A particularly interesting section discusses how Google's PageRank algorithm uses the dominant eigenvector to rank web pages. The links between websites are represented as a connectivity matrix, and the components of its dominant eigenvector provide an estimate of the relative importance of each page.
This is a very clear example of how an apparently abstract idea from linear algebra can underlie a real-world technology used on a massive scale.
The 2023 edition is available under a Creative Commons license. This is another excellent resource that is worth keeping as a reference.
https://collection.bccampus.ca/textbook/qTj4b4Ey
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