The new StatLearning course from Stanford University begins today. Built on the OpenEdX platform, the free massively open online course (MOOC) is an excellent way to get up to speed with state-of-the-art machine learning by two of the foremost experts in the field: professors Trevor Hastie and Robert Tibshirani. Trevor and Rob are also co-authors of the textbook for the class: “An Introduction to Statistical Learning” which is provided as a free download. The deadline for completing all the requirements to get your Statement of Accomplishment is March 21.
The course outline shows the following topics, readings and dates:
Week 1: Introduction and Overview of Statistical Learning (Chapters 1-2, starts Jan 21)
Week 2: Linear Regression (Chapter 3, starts Jan 25)
Week 3: Classification (Chapter 4, starts Feb 1)
Week 4: Resampling Methods (Chapter 5, starts Feb 8)
Week 5: Linear Model Selection and Regularization (Chapter 6, starts Feb 15)
Week 6: Moving Beyong Linearity (Chapter 7, starts Feb 22)
Week 7: Tree-based Methods (Chapter 8, starts Mar 1)
Week 8: Support Vector Machines (Chapter 9, starts Mar 8)
Week 9: Unsupervised Learning (Chapter 10, starts Mar 15)
Enroll in this fantastic learning resource now by clicking HERE.
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