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!
Regression and Classification
Regression and Classification
This course is part of Statistical Learning for Data Science Specialization
Instructor: James Bird
Sponsored by Coursera Learning Team
2,797 already enrolled
Gain insight into a topic and learn the fundamentals.
4.0
(13 reviews)
Intermediate level
Recommended experience
34 hours to complete
3 weeks at 11 hours a week
Flexible schedule
Learn at your own pace
What you'll 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'll gain
Details to know
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Taught in English
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This course is part of the Statistical Learning for Data Science Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 6 modules in this course
Introduction to overarching and foundational concepts in Statistical Learning.
What's included
9 videos2 readings1 discussion prompt
Exploration into assessing models in different situations. How do we define a "best" model for given data?
What's included
6 videos2 programming assignments1 discussion prompt
Introduction to Simple Linear Regression, such as when and how to use it.
What's included
5 videos1 discussion prompt
A deep dive into multiple linear regression, a strong and extremely popular technique for a continuous target.
What's included
6 videos3 programming assignments
What's included
7 videos1 discussion prompt
What's included
8 videos5 programming assignments
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4.0
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DM
5
Reviewed on Apr 29, 2024
Great course with clear and concise explanation. I highly recommend taking the course.
Recommended if you're interested in Data Science
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