Inspired by a recent video by John Green, *My Information Diet, *I’ve decided to revamp my current information intake which consists of frankly far too much Facebook. As a fan of of the medium, I turned by attention to finding some data science/machine learning podcasts and almost instantly came across Partially Derivative whose latest post was entitled *Learning Machine Learning.*

This podcast laid out a syllabus for machine learning under the ideology of combining 3 key areas: Theory, Application and Immersion, before going on to recommend a decent amount of resources. The books are expensive however a large portion of them appear to be online for free in pdf form, here are a selection of the ones mentioned in the podcast.

### Theory

- Intro to Statistical Learning – The first steps
- Elements of Statistical Learning – The Bible of ML
- Deep learning (Yet to find a pdf but this is way off in terms of content)

### Application

- Learn Python – The basics
- Python Cookbook – Code snippets for common problems

### Immersion

I feel like this will provide a pretty good grounding in the field and gives a really decent structure to what I’ll be looking at in the not to distance future.