Andrew Ng, Associate Professor
Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.
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.
Started on: Aug 20th 2012 (10 weeks long)
Workload: 5-7 hours/week
See more at https://www.coursera.org/course/ml