Implementing Decision Trees From Scratch Using Python 2.7

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 […]

Implementing Decision Trees From Scratch Using R

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 […]

Bayesian Methods for Hackers

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.

Stanford’s Machine Learning Project Page

Links: http://cs229.stanford.edu/projects2015.html http://cs229.stanford.edu/projects2014.html http://cs229.stanford.edu/projects2013.html Description:  A list of all the student projects and reports from Stanford’s CS 229 Machine Learning course.   The most recent year has the top projects highlighted.  It is probably not worth exhaustively reading each report unless you want to practice being a C.S. T.A.

Data Science iPython Notebooks by Donne Martin

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.