Decision Trees You can find the iPython Notebook for this post at https://github.com/bryantravissmith/FromScratch/tree/master/SupervisedLearning/DecisionTrees Decision Trees are a non-parametric supervised machine learning algorithm that takes a set of training data and constructs a set of regions in the space of features that is then used to make predictions. These predictions can be continuous values (Decision Tree […]
Main Author: Cam Davidson-Pilon Link: https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers Description: The most hands-on and practical coverage of Bayesian Inference I have been exposed to. It is both a GitHub collection of iPython notebooks and a real living, breathing book.
Author: Donne Martin Link: https://github.com/donnemartin/data-science-ipython-notebooks Description: This is one of the best collection of iPython notebooks. I obsessively went through all of them when I decided I was going to leave teaching and pursue a Data Science career. I found a lot of value in these notebooks. Thank You, Donne.
This is a third part in a series of posts where I am implementing logistic regression from scratch. In the first post I discussed the theory of logistic regression, and in the second post I implemented it in python providing comparison to sklearn. In this post I will be showing how I implemented regularization from […]