Machine Learning

Understanding Dimensions – Linear, Logistic Regression

In the forums of the Coursera course “Machine Learning” by Andrew Ng, there are many questions regarding the dimensions of the input matrix and theta.

Here is an intuitive guide to understanding the dimensions. Click the link below!


Screen Shot 2020-02-23 at 10.36.33 PM

In Summary: 


The dimensions of your input matrix (X) is typically clear and driven from your data. 

[ # of examples, # of input variables ]

The dimensions of theta are driven from your input variables. In linear regression and binary classification, the number of outputs per example is typically 1:

[ # of input variables, # of outputs per example ]

Machine Learning

Notes for Coursera ML Course Week 1-5

I am currently taking the Machine Learning Coursera course by Andrew Ng and I’m loving it! I’ve started compiling my notes in handwritten and illustrated form and wanted to share it here. If you are taking the course you can follow along 🙂

AI Cartoons Week 1 – 5 (PDF download link)

Sign up for a notification on the finished PDF here

* Note these are for Weeks 1-5

If you have feedback, I would love to chat on Discord here

Here’s a sample: