This repository contains python implementations of certain exercises from the course by Andrew Ng.
For a number of assignments in the course you are instructed to create complete, stand-alone Octave/MATLAB implementations of certain algorithms (Linear and Logistic Regression for example). The rest of the assignments depend on additional code provided by the course authors. For most of the code in this repository I have instead used existing Python implementations like Scikit-learn.
Exercise 1 - Linear Regression
Exercise 2 - Logistic Regression
Exercise 3 - Multi-class Classification and Neural Networks
Exercise 4 - Neural Networks Learning
Exercise 5 - Regularized Linear Regression and Bias v.s. Variance
Exercise 6 - Support Vector Machines
Exercise 7 - K-means Clustering and Principal Component Analysis
Exercise 8 - Anomaly Detection and Recommender Systems
https://www.coursera.org/learn/machine-learning/home/welcome
The orginal author of this repository provide excellent codes for me to learn more about Andrew Ng's course. I am grateful to the author and Andrew for their generosity. Although we can implement all of these algorithms with existing libraries, it is always a great pleasure in understanding them from the scratch. I manually coded these programs and modified a bit according to my own coding style.