It's an incredibly difficult task to type notes on complex machine learning algorithms, so I'm resorting to paper. I might scan those sheets and upload them here, but for now, I'm in the middle of the second week of Andrew Ng's Machine Learning course, and it's going well.
To get myself back on track with the data science track, I scheduled time during February and March to take multiple courses at a time. However, this Reddit post had a user mention something about dependencies between the courses. After some digging, I found this pdf document, outlining the "hard" and "soft" dependencies between the courses in the track.
TL;DR: Take the track's courses in the following blocks:
| Block 1 | Block 2 | Block 3 |
|---|---|---|
| The Data Scientist's Toolbox | Getting and Cleaning Data | Regression Models |
| R Programming | Exploratory Data Analysis | Practical Machine Learning |
| Reproducible Research | Developing Data Products | |
| Statistical Inference |
Note: The sheet says of Practical Machine Learning:
This course has hard dependencies on R Programming, The Data Scientist's Toolbox and Regression Models. It has a soft dependency on Exploratory Data Analysis.
However, since I'm currently taking Machine Learning, I imagine Practical Machine Learning won't be too bad. Let's hope I'm right!