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This is meant to be used as a general tutorial for beginners with some experience in Python or R.
Step by step tutorial on using K-Means clustering to analyze your customer base.
Why learning new things is hard, plus four fundamental differences between Excel and R
What is support vector machine and why should you use it?
This cheat sheet is a handy reference for using the Scikit-Learn Python package
This cheat sheet is a quick reference for Pandas beginners
An introduction to working with random forests using Python
Using a deep neural net to detect what's in an image.
Use SQL but new to python? Check out pandasql. An easy way for SQL users to learn pandas.
What is split testing and how does it work in ScienceOps?
Analysis of iPhone step data using pandas timeseries, Rodeo, and ggplot
Moving from R to Python? Make sure you know about these packages.
A quick introduction to grouping in pandas with NYC Citi Bike data
Diving into the RPy2 packages and using both R and Python in the same workspace.
How to normalize data using feather and pandas.
Swimming with Sharks! An intro to Geoplotting with Python
ROC curves are a great tool for binary classification. Learn more in this post!
Introduction to plotting and graphics in R (without ggplot2)
A high level introduction to what linear regression is and how it works.
A highlight of 11 lesser-known Python libraries, that even you experienced Pythonistas may have not seen!
Running code in parallel is tricky. This post shows how to quickly (and easily) parallelize your R code.
R can be a bit bloated someitmes. Learn how to make your R models more effecient.
How to implement your own naive bayes classifier in Python and a detailed explanation of how it all works.