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Statistical Learning

Learn some of the main tools used in statistical modeling and data science. We cover both traditional as well as exciting new methods, and how to use them in R.

Statistical Learning

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39,806 already enrolled!
Estimated 9 weeks
3–5 hours per week
Self-paced
Progress at your own speed
Free
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About this course

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This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).

This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data analysis. Computing is done in R. There are lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter.

The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013). The pdf for this book is available for free on the book website.

At a glance

  • Language: English

What you'll learn

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  • Overview of statistical learning
  • Linear regression
  • Classification
  • Resampling methods
  • Linear model selection and regularization
  • Moving beyond linearity
  • Tree-based methods
  • Support vector machines
  • Unsupervised learning

About the instructors

Frequently Asked Questions

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Do I need to buy a textbook?

No, a free online version of An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013) is available from that website. Springer has agreed to this, so no need to worry about copyright. Of course you may not distribiute printed versions of this pdf file.

Is R and RStudio available for free?

Yes. You get R for free from http://cran.us.r-project.org/. Typically it installs with a click. You get RStudio from http://www.rstudio.com/, also for free, and a similarly easy install.

How many hours of effort are expected per week?

We anticipate it will take approximately 3-5 hours per week to go through the materials and exercises in each section.

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