Computational biology PhD researcher. Interested in science, software development, and machine learning. I write about medical research at BioSky.co and contribute content to a variety of additional publications.

CVAboutLately, 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**

**MOOCS**

**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)**