My goal is to explain and implement fundamental machine learning algorithms in a clear and concise way using Python. If I am successful then you will walk away with a little better understanding of the algorithms or at the very least some code to serve as a jumping off point when you go to try them out for yourself.

I cover a total of 8 different machine learning algorithms. Feel free to jump around or skip an algorithm if you’ve got it down. Use this guide however your heart desires. Here's how it breaks down:

1. Linear Regression
2. Logistic Regression
3. Decision Trees
4. Support Vector Machines
5. K-Nearest Neighbors
6. Random Forests
7. K-Means Clustering
8. Principal Components Analysis

This repo is based off a popular Medium post (100,000+ views). If you stumbled upon this, I highly recommend checking out the original post first and then coming back:

The Hitchhiker's Guide to Machine Learning Algorithms in Python