coursera-ML-in-python

General Purpose Machine Learning Algorithms and Applications in Python developed using numpy, scipy and matplotlib libraries implemented in Jupyter Notebooks.

This repository contains:

• Python implementation of certain assignments I completed as part of the Machine Learning course by Andrew Ng on Coursera. The assignments were originally implemented in Octave.

Contents

• Linear Regression
  • Univariate Linear Regression
  • Multivariate Linear Regression
  • Multivariate Linear Regression using Normal Equations
• Logistic Regression
  • Basic Logistic Regression Implemntation
  • Logistic Regression using Polynomial Features with Regularization
  • Handwritten Digit Recognition using one-vs-all Logistic Regression
• Handwritten Digit Recognition using Neural Networks
• Bias vs Variance
• K-means Clustering
• Principal Component Analysis
• Anomaly Detection

To Do

• K-means Clustering
• Principal Component Analysis
• Anomaly Detection