cdeweyx / Hitchhikers-Guide-Machine-Learning

Guide explaining and implementing fundamental machine learning algorithms in Python

The Hitchhiker's Guide to Machine Learning in Python

Goal

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.

Breakdown

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

See the Post

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