Regression and Classification

Regression and Classification

This course is part of Statistical Learning for Data Science Specialization

Instructor: James Bird

3,864 already enrolled

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16 reviews

Intermediate level

Recommended experience

Flexible schedule

Learn at your own pace

Build toward a degree

Learn more

16 reviews

Intermediate level

Recommended experience

Flexible schedule

Learn at your own pace

Build toward a degree

Learn more

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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Taught in English

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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
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Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. 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.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.