There are a lot of very good resources to learn ML that are accessible for free. Here I list some of them.
Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares—Boyd 2018. It’s a not-very-deep (but much deeper than your average MOOC) linear algebra textbook with the focus on ML and ML-adjacent applications. For instance, it covers k-means clustering, least squares data fitting, least squares classification. Boyd is known for being the authour of the best introductory textbook on convex optimization, hence this textbook is probably good as well.
Linear algebra and learning from data—Strang 2019. This one assumes some knowledge of linear algebra and teaches how to use all that on contemporary computers efficiently and also it teaches many ML and ML-adjacent methods using that knowledge, including (stochastic) gradient descent, neural networks, backpropagation, some statistics. Strang is known for being the author of one of the best linear algebra textbooks “Introduction to linear algebra”, hence this textbook is probably good as well. There is also a free MIT opencourseware course https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/ that follows this textbook.
The elements of statistical learning. Data mining, inference, and prediction 2nd edition—Hastie and Tibshirani 2008. This one covers A LOT OF ML, although all of it is pre-neural-network-revolution. Most exercises are of form “prove this theorem” rather than “implement this in code”.
Pattern recognition and machine learning—Bishop 2006
Probabilistic Machine Learning: An Introduction—Murphy 2021
Deep learning—Goodfellow, Bengion, and Courville 2016
AI: a modern approach 4th edition—Russell, Norvig 2020
Some of these are free but most are not. However, you can pirate them off libgen.
There are a lot of very good resources to learn ML that are accessible for free. Here I list some of them.
Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares—Boyd 2018. It’s a not-very-deep (but much deeper than your average MOOC) linear algebra textbook with the focus on ML and ML-adjacent applications. For instance, it covers k-means clustering, least squares data fitting, least squares classification. Boyd is known for being the authour of the best introductory textbook on convex optimization, hence this textbook is probably good as well.
Linear algebra and learning from data—Strang 2019. This one assumes some knowledge of linear algebra and teaches how to use all that on contemporary computers efficiently and also it teaches many ML and ML-adjacent methods using that knowledge, including (stochastic) gradient descent, neural networks, backpropagation, some statistics. Strang is known for being the author of one of the best linear algebra textbooks “Introduction to linear algebra”, hence this textbook is probably good as well. There is also a free MIT opencourseware course https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/ that follows this textbook.
The elements of statistical learning. Data mining, inference, and prediction 2nd edition—Hastie and Tibshirani 2008. This one covers A LOT OF ML, although all of it is pre-neural-network-revolution. Most exercises are of form “prove this theorem” rather than “implement this in code”.
Pattern recognition and machine learning—Bishop 2006
Probabilistic Machine Learning: An Introduction—Murphy 2021
Deep learning—Goodfellow, Bengion, and Courville 2016
AI: a modern approach 4th edition—Russell, Norvig 2020
Some of these are free but most are not. However, you can pirate them off libgen.