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!
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Regression and Classification
University of Colorado BoulderAbout this Course
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Shareable Certificate
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Intermediate Level
Intro Statistics and Foundational Math
Approx. 35 hours to complete
English
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
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Intermediate Level
Intro Statistics and Foundational Math
Approx. 35 hours to complete
English
Offered by
Start working towards your degree
This Course is part of an online degree program offered by the University of Colorado Boulder. When you enroll in a for-credit non-degree course through the university and complete it online, it counts as credit hours towards a degree at CU-Boulder. All you have to do is apply through the university.
Syllabus - What you will learn from this course
1 hour to complete
Statistical Learning Introduction
1 hour to complete
9 videos (Total 37 min), 1 reading
7 hours to complete
Accuracy
7 hours to complete
6 videos (Total 34 min)
1 hour to complete
Simple Linear Regression
1 hour to complete
5 videos (Total 30 min)
10 hours to complete
Multiple Linear Regression
10 hours to complete
6 videos (Total 35 min)
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