Here at PerceptiLabs we love exploring all sorts of machine learning (ML) approaches. And if you've poked around our site in the last little while, you may have come across our Machine Learning Handbook. It's a free resource that you can download and use to become more familiar with approaches like linear regression, decision trees, k-nearest neighbor, support vector machines (SVMs), clustering, and of course, neural networks.

The handbook reviews the architecture and math behind these powerful algorithms. And while the formulas may look intimidating to implement, fear not, because we've released some free source code on GitHub that shows how several of the ML approaches can be implemented in Python.

The code released includes:

  • neural networks
  • K-means
  • decision tree classifiers
  • linear regression

So be sure to check out the code, and post any questions or comments you may have on our public Slack channels.