In This Repository, the solution of the 4 course-specialization of Machine Learning that has been made in 2016 by the husband and wife couple Carlos Guestrin and Emily Fox, previously of Carnegie Mellon and now of the University of Washington. And here is a link to the specialization.
This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.
The Specialization consists of 4 courses:
- Course (1): Machine Learning Foundations: A Case Study Approach.
- Course (2): Machine Learning: Regression.
- Course (3): Machine Learning: Classification.
- Course (4): Machine Learning: Clustering & Retrieval.
I've come away frustrated that the courses emphasized the use of Graphlab Create, a closed source software from Guestrin’s company Turi (formerly Dato) over open source libraries like Scikit-Learn. It’s true that Graphlab was not a requirement to complete the coursework, but using an open source solution such as Scikit-Learn added a significant level of commitment given that the lessons had been tailored with starter code to Graphlab.
This specialization was meant to be a six-courses specialization, but due to some problems they decided to make it a four-course specialization. I think one of the problems is that Turi was sold to Apple for 200 million dollars according to the geekwire site.