50+ days of Machine Learning

 Hello :) Today is Day 89! A quick summary of today: covered Classification, Resampling methods, Tree-based models and Some considerations in high dimensions from  An Introduction to Statistical Learning Besides the below and yesterday's notes, for not I just read some of the other material of the book. For a while I had the Probabilistic ML: An introduction by Kevin Muprhy on my list (saw it recommended a lot), and it covers  and I am particularly interested in the Foundations part, seeing how the basics are 'born'. But I will start that tomorrow ^^ So ~ here are my notes from the ISLP book for the 4 chapters covered today: Chapter 4: Classification   Chapter 5: Resampling methods Chapter 6: Considerations in high dimensions Chapter 8: Tree-based methods  That is all for today! See you tomorrow

 Hello :) Today is Day 88! A quick summary of today: Covered Chapter 2: Statitiscal learning, and Chapter 3: Linear regression of the infamous  ISLP  book Today I decided to go over this book that I keep seeing recommended for the basics of machine learning. As a fan of basics, it grabed my interest.  My notes are below: Chapter 2: Statistical learning Chapter 3: Linear regression That is all for today! See you tomorrow :)