I recently came up with an idea. Insteading of summarizing lectures by hands, how about using markdown and post on blog?

Contents: More types of gradient descent, online learning, pipeline and more

Contents: Anomaly detection and Recommender System

Contents: Unsupervised learning algorithm —- K-means and Principal Component Analysis

Contents: Support Vector Machine

BAD NEWS: Sadly, Couresara is no longer providing reading notes from this chapter and onwards for some reason (e.g. laze). That means I have to make the ENTIRE note by myself.

Contents: Evaluation and diagnosis for hypothesises.

Contents: Neural Networks.

Contents: Linear Regression, logistic regression, gradient descent and more.