Implementing Decision Trees From Scratch Using Python 2.7

Decision Trees You can find the iPython Notebook for this post at 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 […]

Bayesian Methods for Hackers

Main Author: Cam Davidson-Pilon Link: 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.

Data Science iPython Notebooks by Donne Martin

Author:  Donne Martin Link: 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.

Implementing Logistic Regression From Scratch – Part 3 : Regularization

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 […]