Guide explaining and implementing fundamental machine learning algorithms in Python
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README.md

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