README.md

learning-ml

Jupyter notebooks for the math and implementations of popular machine learning algorithms

Introduction

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.

Table of Contents

0. 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
1. Linear Regression
   • Use of Adjusted R-Squared
   • Derivation of the Normal Equation
2. 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
3. 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
4. Clustering
   • https://en.wikipedia.org/wiki/Cluster_analysis
   • https://stanford.edu/~shervine/teaching/cs-229/cheatsheet-unsupervised-learning#clustering

Textbooks and Other Resources

1. Introduction to Statistical Learning
2. Elements of Statistical Learning
3. Machine Learning Series - Paul. G. Allen School