50+ days of Machine Learning

 Hello :) Today is Day 124! A quick summary of today: Introduction to Machine learning by  Volodymyr Kuleshov  from Cornell Univeristy  Naive Bayes practical session by Richard Willis from King's College London Factorization methods by  Cho-Jui Hsieh  from UCLA Introduction to Machine learning by Volodymyr Kuleshov from Cornell Univeristy  It was really an amazing introduction. Personally it did not cover new stuff for me, but as an intro I believe it was top. What is supervised ML, OLS, Covered Non-Linear Least Squares, Overfitting, Regularization Naive Bayes practical session by Richard Willis from King's College London The interesting bit was implementing a Naive Bayes classification model from scratch.  I feel like this is one of the simplest explanations of Bayes theorem that I have seen/read so far (of prior, likelohood and posterior). Richard Willis is a Phd Implementation Computing the prior Computing the likelihoods  Computing log posterior Complete NaiveBayesClassifi