50+ days of Machine Learning

 Hello :) Today is Day 90! A quick summary of today: covered the first chapter of the Foundations part of  Probabilistic Machine Learning: An Introduction set up my environment and covered the basics from the 0th colab for Stanford's XCS224W: ML with Graphs Probabilistic Machine Learning: An Introduction Chapter 2: Probability: Univariate Models Today I got added to the slack channel for XCS224W: ML with Graphs by Stanford, and I went over setting up the environment and over the 0th colab - some basics about graphs. I am not allowed to share any of the code from the colabs, but I found this similar tutorial  that is public and I can talk over how I improved upon it. (I need to go over the animation part of this tutorial and showing the nodes in 3d space) To avoid any risk of a lawyer sending me an email, I will just say how I improved upon the above tutorial.  After training a GNN on KarateClub data from pytorch_geometric, the result looked like And there was a conclusion under it

 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 :)