Statistical Learning with Python
- Free to audit
- Paid certificate
- Course certificate
- Beginner
- 11 weeks
Statistical Learning with Python is StanfordOnline's edX course by Trevor Hastie, Robert Tibshirani and Jonathan Taylor, following their textbook An Introduction to Statistical Learning with Applications in Python, which the course FAQ says is free to download with the publisher's agreement. The syllabus runs from an overview of statistical learning through linear regression, classification, resampling methods, linear model selection and regularisation, moving beyond linearity, tree-based methods, support vector machines, deep learning, survival modelling, unsupervised learning and multiple testing, with labs in Python and Jupyter using the accompanying ISLP package. edX lists it as self-paced, beginner level and eleven weeks long at three to five hours a week.
You can audit for free, which gives access to the material for a limited time but not to graded assignments or a certificate; the verified track with an edX certificate costs $186. A parallel course teaches the same material in R.
What you’ll learn
- Fit and interpret linear regression and classification models in Python
- Use resampling methods and model selection with regularisation
- Move beyond linearity with splines, generalised additive models and tree-based methods
- Train support vector machines and deep learning models
- Apply survival modelling, unsupervised learning and multiple testing
Who it’s for
Learners with basic Python and some statistics who want the standard introduction to supervised and unsupervised learning from the textbook's authors.
Source: Stanford Online (opens in a new tab) · Verified · Report a change
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