In this class,I learned about the most effective machine learning techniques, and gained practice implementing them and getting them to work for myself. More importantly,I learned about not only the theoretical underpinnings of learning, but also gained the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, I learned about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.  

This course provides a broad introduction to machine learning, data mining, 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).

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.

Link to the certificate