Summary of algorithms and tools:

Check out overview here (https://alekzdz.blogspot.com/2018/08/solutions-to-andrew-ngs-machine.html)

Supervised Learning

* Linear Regression - linear and polynomial fit
* Logistic Regression - classification
	* Mulitclass classification
* Neural Network - classification
* Support Vector Machines - Large margin classifiers
	Gaussian Kernel - similarity function

Unsupervised Learning

* K-Means - clustering
* Principal Component Analysis - dimensional reduction
* Anomaly Detection - Gaussian/normal distribution

Applied Algorithms

* Recommendation systems

Diagnostics

* Underfitting Bias
* Overfitting Variance
* Convex cost functions
* Precision, Recall, (true positives, true negatives, false positives, false negatives), F1 score

by Alek Zdziarski