These are the programming assignments from Coursera's Machine Learning course taught by Andrew Ng.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
- Linear Regression with Multiple Variables [100/100]
- Logistic Regression [100/100]
- Multi-class Classification and Neural Networks [100/100]
- Neural Network Learning [100/100]
- Regularized Linear Regression and Bias/Variance [100/100]
- Support Vector Machines [100/100]
- K-Means Clustering and PCA [100/100]
- Anomaly Detection and Recommender Systems [100/100]