My learnings and home work exercises
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Mathematics for ML book https://mml-book.github.io/book/mml-book.pdf
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Theory and code https://github.com/Machine-Learning-Tokyo/Math_resources
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Python, scipy and numpy https://cs231n.github.io/python-numpy-tutorial/
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Discrete maths https://www.coursera.org/specializations/discrete-mathematics#courses
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Maths for ML and maths for datascience https://www.coursera.org/specializations/mathematics-machine-learning https://www.coursera.org/specializations/mathematics-for-data-science#courses
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Udemy https://www.udemy.com/course/linear-algebra-theory-and-implementation/
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Comprehensive ML https://www.youtube.com/watch?v=w2OtwL5T1ow&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6&index=1
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Hard Statistics and Data Science Concepts Visually Explained https://towardsdatascience.com/hard-statistics-and-data-science-concepts-visually-explained-de7325c2e9ef
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Linear algebra with visualization http://immersivemath.com/ila/
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Stats with visualization https://setosa.io/ev/