Introduction to Statistical Learning will explore concepts in statistical modeling, such as when to use certain models, how to tune those models, and if other options will provide certain trade-offs. We will cover Regression, Classification, Trees, Resampling, Unsupervised techniques, and much more!
Offered By
Regression and Classification
University of Colorado BoulderAbout this Course
Intro Statistics and Foundational Math
What you will learn
Express why Statistical Learning is important and how it can be used.
Identify the strengths, weaknesses and caveats of different models and choose the most appropriate model for a given statistical problem.
Determine what type of data and problems require supervised vs. unsupervised techniques.
Skills you will gain
- Statistics
- Data Science
- R Programming
Intro Statistics and Foundational Math
Offered by

University of Colorado Boulder
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
Start working towards your Master's degree
Syllabus - What you will learn from this course
Statistical Learning Introduction
Introduction to overarching and foundational concepts in Statistical Learning.
Accuracy
Exploration into assessing models in different situations. How do we define a "best" model for given data?
Simple Linear Regression
Introduction to Simple Linear Regression, such as when and how to use it.
Multiple Linear Regression
A deep dive into multiple linear regression, a strong and extremely popular technique for a continuous target.
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