/mathsforbrain

Course notes and solved problems from Coursera, Udemy and books

Primary LanguageJupyter Notebook

My math workout area

My learnings and home work exercises

Resources

  1. Mathematics for ML book https://mml-book.github.io/book/mml-book.pdf

  2. Theory and code https://github.com/Machine-Learning-Tokyo/Math_resources

  3. Python, scipy and numpy https://cs231n.github.io/python-numpy-tutorial/

  4. Discrete maths https://www.coursera.org/specializations/discrete-mathematics#courses

  5. 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

  6. Udemy https://www.udemy.com/course/linear-algebra-theory-and-implementation/

  7. Blogs https://towardsdatascience.com/mathematical-programming-a-key-habit-to-built-up-for-advancing-in-data-science-c6d5c29533be

  8. Comprehensive ML https://www.youtube.com/watch?v=w2OtwL5T1ow&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6&index=1

  9. Hard Statistics and Data Science Concepts Visually Explained https://towardsdatascience.com/hard-statistics-and-data-science-concepts-visually-explained-de7325c2e9ef

  10. Linear algebra with visualization http://immersivemath.com/ila/

  11. Stats with visualization https://setosa.io/ev/