This is my solution to all the projects of Machine-Learning taught by Andrew Ng.
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). Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course drew from numerous case studies and applications, building perception, control, text understanding (web search, anti-spam), computer vision, audio.