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Coursera Machine Learning

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

References:

https://www.coursera.org/learn/machine-learning/home/welcome

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].

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