Hi everyone! Great to be back! Decision tree classifiers are intuitive, interpretable, and one of my favorite supervised learning algorithms. In this episode, I'll show you how to write a decision tree classifier from scratch in pure Python. I'll introduce concepts like decision tree learning, Gini impurity, and information gain. Then we'll code it all up. Understanding how to achieve this was helpful for me when I first started studying machine learning, and I hope it will prove useful for you too.

You can find the code from this video here:
https://goo.gl/UdZoNr
https://goo.gl/ZpWYzt

Books!
Practical Machine Learning with Scikit-Learn and TensorFlow https://goo.gl/kM0anQ

Follow Josh on Twitter: https://twitter.com/random_forests

You can find more machine learning recipes here: https://goo.gl/KewA03

Subscribe to the Google Developers channel: http://goo.gl/mQyv5L

Please take the opportunity to connect with your friends and family and share this video with them if you find it useful.