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:
- Linear Regression
- Logistic Regression
- Decision Trees
- Support Vector Machines
- K-Nearest Neighbors
- Random Forests
- K-Means Clustering
- 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
