Jupyter notebooks for the math and implementations of popular machine learning algorithms
This repo is a collection of notebooks that contain Python based implementations of various fundamental machine learning algorithms. This initially started of as a part of the ml-deepdive series, taken up by the MSDS 2017 Cohort at UW, but is now maintained independently as a personal learning exercise.
- Prerequisites - Linear Algebra
- http://cs229.stanford.edu/section/cs229-linalg.pdf
- http://www.deeplearningbook.org/contents/linear_algebra.html
- http://www.deeplearningbook.org/contents/prob.html
- http://www.deeplearningbook.org/contents/numerical.html
- http://parrt.cs.usfca.edu/doc/matrix-calculus/index.html (a short primer on matrix calculus for machine learning)
- http://www.deeplearningbook.org/contents/ml.html (optional)
- http://students.brown.edu/seeing-theory/#firstPage (A little basic but fun)
- http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ (again, a little basic, but fun)
- 3Blue1Brown - Linear Algebra Series
- Linear Regression
- Decision Trees and Random Forests
- http://scikit-learn.org/stable/modules/tree.html
- https://web.stanford.edu/class/stats202/content/lec19.pdf
- Gentle Intro to Bagging - I
- Gentle Intro to Bagging - II
- https://www.datascience.com/resources/notebooks/random-forest-intro
- Logistic Regression
- https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/pdfs/40%20LogisticRegression.pdf
- https://www.stat.cmu.edu/~cshalizi/uADA/12/lectures/ch12.pdf
- https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faq-how-do-i-interpret-odds-ratios-in-logistic-regression
- Clustering