Resources

Lately, myself and a friend have become rather interested in learning more about machine learning. I’ve been trying to start a collection of learning materials that I either found useful or mean to go through at some point, and thought I’d write a post about it. I’m hoping this can help people who are just starting out and those who know more than me could comment and point me towards some additional material I can link to and look into myself.

**Update** Had a great response on Reddit, thanks so much for all the suggestions that have flooded in! I’ve added quite a few new links that looked good, and am currently reviewing some other materials I’ll link to soon. This list will continue to evolve with links to new content as I find them (or am alerted by others). Follow me on Twitter for updates and new posts on machine learning, research and statistics.

Teaching Materials

• Introduction to machine learning with scikit-learn
• Scikit-learn Machine Learning with Python and SKlearn
• Jake VanderPlas – Machine Learning with Scikit-Learn (I) – PyCon 2015
• Olivier Grisel – Machine Learning with Scikit-Learn (II) – PyCon 2015
• Sarah Guido – Hands-on Data Analysis with Python – PyCon 2015
• In-depth introduction to machine learning in 15 hours of expert videos
• R and Data Mining
• General Assembly’s Data Science course in Washington, DC
• Neural networks class
• Machine Learning
• Introduction to Statistical Machine Learning 2015

Talks

• Melanie Warrick: How to Get Started with Machine Learning – PyCon 2014
• Ned Jackson Lovely: Enough Machine Learning to Make Hacker News Readable Again – PyCon 2014
• Brandon Rhodes – Pandas From The Ground Up – PyCon 2015
• Melanie Warrick – Neural Nets for Newbies – PyCon 2015
• Kyle Kastner – Machine Learning 101 – PyCon 2015

iPython Notebooks

• Introduction to Linear Regression
• Logistic Regression with scikit-learn
• Learn Data Science
• Machine Learning Tutorials
• Machine Learning
• Machine Learning
• Machine Learning 101
• Introduction to Scikit-Learn
• Learn Pandas [useful if you’re going to be using Python for machine learning]

R Markdown

• R Markdowns

MOOCS

• Intro to Machine Learning
• Machine Learning
• Learning from Data

Interesting Links

• Detecting Fraudulent Skype Users via Machine Learning
• Machine learning the hard way — a story about ponies
• The Unreasonable Effectiveness of Recurrent Neural Networks
• Ultimate guide for Data Exploration in Python using NumPy, Matplotlib and Pandas
• How To Run Linear Regression In Python SciKit-Learn
• Big Data, Data Mining and Machine Learning: Under the Hood
• Choosing the right estimator [in Scikit-Learn]

OpenCV Machine Learning

• Understanding k-Nearest Neighbour
• OCR of Hand-written Data using kNN
• Understanding K-Means Clustering
• K-Means Clustering in OpenCV
• Understanding SVM
• OCR of Hand-written Data using SVM

Books

• Data Science at the Command Line
• A Programmer’s Guide to Data Mining
• Doing Data Science
• Machine Learning for Hackers
• Practical Data Science with R
• Introduction to Data Science with R
• Scikit-Learn Cookbook
• Practical Data Science with R
• An Introduction to Statistical Learning
• The Elements of Statistical Learning
• Pattern Recognition and Machine Learning
• Programming Collective Intelligence

Podcasts

• Linear Digressions
• Data Skeptic
• Talking Machines
• The Data Show
• Programming Throwdown [not strictly machine learning but very informative about programming]

Amazon Web Services (AWS)

• Regression Prediction using AWS Machine Learning

I’ve been following some of the great data school tutorials and I recently read a couple of interesting posts about teaching programming and data science on their website:

• Should you teach Python or R for data science?
• Lessons learned from teaching an 11-week data science course

The second article is a little longer, but well worth the read. When I first started trying to learn about machine learning, I thought R would be the natural choice. However, I’ve become a real fan of Python’s Scikit-Learn (Six reasons why I recommend scikit-learn) lately and have continued my learning in Python. I can definitely see why they made that decision with their teaching, and the ability to use the Anaconda Python installation to get up and running definitely makes a lot of sense.

Getting back to the original purpose of this post, I liked the teaching idea he discusses in the article of starting the class off with a motivating example – in their case they explored the iris dataset and used the K-nearest neighbours algorithm. I also thought it was a great idea to use data retrieval as a lesson opportunity rather than merely downloading a bunch of CSV files from a website.

Finally, one thing I think would have been handy would be for them to give a tour of Unix/Linux and Bash as part of the course materials, but then I guess you have to draw the content line somewhere when time is limited.