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I have recently taken Udacity’s free Introduction to Machine Learning course in an attempt to update and upgrade my current skill set. Upon completing the course, have decided to write a post about what I studied and the projects I undertook to learn the course material and hopefully advance in my profession:-
Lesson 1
Lesson 1 of the course was the introductory lesson, which scoped out what I was expected to learn on the course. The course was intended to last about four months in duration, but I was in a hurry to finish it because I wanted to grasp new topics and learn as much as I could about machine learning to help me to achieve my goal of winning a Kaggle competition at some point. In addition, in August Kaggle is having a 30 Days of Machine Learning course and I wanted to complete the Udacity course before undertaking this new Kaggle course.
Lesson 2
Lesson 2 covers the Naive Bayes classifier, which is a simple probabilistic classifier based on Bayes theorem with strong independence assumptions between the features. This type of classifier is simple, yet accurate if it is coupled with kernel density estimation.
The link to the blog post I wrote on Lesson 2, Naive Bayes, can be found here…
