• Mathmatics for Machine Learning
• control versus adjust
• 3blue1brown: essence of linear algebra
  • This provides a great conceptual overview and awesome visualizations, I would recommend watching these videos first, and then again after you've completed some of the other links.
• Khan Academy: least squares approximation
  • A great complement to 3blue1brown, Sal walks through concrete examples of least squares approximation (which is essentially the linear algebra form of regression)
• Introduction to Statistical Learning: Chapter 3
  • I've read the first three chapters in full, and the 3rd chapter on regression really opened up my understanding on how to interpret regression parameters
• Elements of Statistical Learning: Chapter 3
  • A more formal mathematical understanding of linear regression, I haven't finished this yet.
• MumfordBrainStats: Regression overview
  • Great series by Jeanette to understand regression in the context of brain science
• John Hopkins Data Science Track: Regression Models
  • Currently going through this class, so far it's awesome!
• zstatistics: regression
  • I used their 2nd video to understand degrees of freedom more intuitively
• Linear Algebra for Machine Learning
• Calculus for Machine Learning
• General Linear Model For Neuroimaging (FSL)