This is a brief review of the online course "Machine Learning Foundations: A Case Study Approach" from the University of Washington. I must disclose that although I worked through the examples in the course, I could not complete the assessments since I was not taking the course for credit.

The course was run by Carlos Guestrin, Amazon Professor of Machine Learning Computer Science and Engineering and Emily Fox, Amazon Professor of Machine Learning Statistics. Carlos is the co-founder and CEO of Dato, Inc (formerly GraphLab, Inc).

The ML (Machine Learning) blackbox used in the course was from GraphLab as well as the iPython Notebook programing language. These modules were downloaded to one's own computer which made it possible to play along and make coding mistakes. The fundamental modelling  design is illustrated in the diagram below -- using predicting house prices.

The examples used were:

• Regression:  Predicting house prices
• Classification:  Analyzing consumer sentiment
• Clustering and Similarity:  Retrieving documents
• Recommending Products
• Deep Learning:  Searching for images

This course provided an excellent introduction into the world of machine learning. As the open source tools of ML become more sophisticated and easier to use, it opens the doors for anyone with an interest in data analysis or modelling for mind-blogging applications. Below is a video mashup of Carlos and Emily discussing the future of ML.

What is the bottom line about machine learning? It doesn't require any coding, it uses a tool-kit of statistical methods and the data is split into 'training data' and 'test data' where the training data is used to fit into a regression or a nearest-neighbor (or any other) statistic by iteration until there is convergence in the error and then this is evaluated by using the test data. The modelling is theory-free and only requires good data.