Welcome to a Friendly Guide to Machine Learning!

Just enough theory to get you going.

Contents:

• A Complete Guide to Matrix Notation and Linear Regression
  • The Sum of Squared Error Loss
  • Going into Matrix Notation
  • Deriving a Closed-form Solution
  • A NumPy Example
  • Using sklearn
• Linear Regression with Regularization
  • The Need for Regularization
  • L2 and L1 Penalty
  • Visualizing the Loss Surface with Regularization
  • Which one to use, L1 or L2?