Machine Learning on Coursera

Coursera learning-related project about Machine Learning.

Created by Standford University.

Instructor: Andrew Ng

Introduction

This repository contains my programming assignments for Andrew Ng's Coursera Class.

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include:

1. Supervised learning: parametric/non-parametric algorithms, support vector machines, kernels, neural networks.

2. Unsupervised learning: clustering, dimensionality reduction, recommender systems, deep learning.

3. Best practices in machine learning: bias/variance theory; innovation process in machine learning and AI.

Programming Assignments

There were 8 programming exercises for the course covering the following topics:

• 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