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
Give your career the gift of Coursera Plus with $160 off, billed annually. Save today.
Regression and Classification
This course is part of Statistical Learning for Data Science Specialization
Instructor: James Bird
2,561 already enrolled
Included with 
Gain insight into a topic and learn the fundamentals.
4.0
(13 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 34 hours
Learn at your own pace
Build toward a degree
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
Shareable certificate
Add to your LinkedIn profile
Taught in English
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
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.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
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
Instructor
Offered by
Recommended if you're interested in Probability and Statistics
University of London
Coursera Project Network
Duke University
Build toward a degree
This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
Why people choose Coursera for their career
Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."
Learner reviews
Showing 3 of 13
4.0
13 reviews
- 5 stars
64.28%
- 4 stars
7.14%
- 3 stars
0%
- 2 stars
7.14%
- 1 star
21.42%
DM
5
Reviewed on Apr 28, 2024
New to Probability and Statistics? Start here.
Open new doors with Coursera Plus
Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Advance your career with an online degree
Earn a degree from world-class universities - 100% online
Join over 3,400 global companies that choose Coursera for Business
Upskill your employees to excel in the digital economy
Frequently asked questions
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.