"Machine Learning" Exercises On Coursera

Introduction

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

Supervised Learning
- Parametric, Non-Parametric Algorithms
- Support Vector Machines
- Kernels
- Neural Networks
Unsupervised Learning
- Clustering
- Dimensionality Reduction
- Recommender Systems
- Deep Learning

Name		Name	Last commit message	Last commit date
Latest commit History 101 Commits
Badges		Badges
Exercise-1 Linear Regression		Exercise-1 Linear Regression
Exercise-2 Logistic Regression		Exercise-2 Logistic Regression
Exercise-3 Multi-class Classification and Neural Networks		Exercise-3 Multi-class Classification and Neural Networks
Exercise-4 Neural Network Learning		Exercise-4 Neural Network Learning
Exercise-5 Regularized Linear Regression and Bias v.s. Variance		Exercise-5 Regularized Linear Regression and Bias v.s. Variance
Exercise-6 Support Vector Machines		Exercise-6 Support Vector Machines
Exercise-7 K-Means Clustering and PCA		Exercise-7 K-Means Clustering and PCA
Exercise-8 Anomaly Detection and Recommender Systems		Exercise-8 Anomaly Detection and Recommender Systems
Resources		Resources
.gitignore		.gitignore
CITATION.cff		CITATION.cff
Cover.png		Cover.png
LICENSE		LICENSE
README.md		README.md