Regression and Classification

Regression and Classification

This course is part of Statistical Learning for Data Science Specialization

Instructor: James Bird

Access provided by EDGE Group

3,754 already enrolled

6 modules

Gain insight into a topic and learn the fundamentals.

16 reviews

Intermediate level

Recommended experience

4 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

6 modules

Gain insight into a topic and learn the fundamentals.

16 reviews

Intermediate level

Recommended experience

4 weeks to complete

at 10 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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

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

There are 6 modules in this course

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!

This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

Introduction to overarching and foundational concepts in Statistical Learning.

What's included

9 videos3 readings1 discussion prompt

9 videos Total 37 minutes

Introduction and Welcome 1 minute
Supervised vs. Unsupervised 6 minutes
Notation Overview 4 minutes
Overview Example & Discussion 4 minutes
Prediction 5 minutes
Inference 4 minutes
Parametric Methods 3 minutes
Interpretability vs. Flexibility 6 minutes
Quantitative vs. Qualitative 3 minutes

3 readings Total 21 minutes

Course Updates and Accessibility Support 1 minute
Earn Academic Credit for your Work! 10 minutes
Course Support 10 minutes

1 discussion prompt Total 10 minutes

Introduce Yourself 10 minutes

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

6 videos Total 34 minutes

Model Accuracy 6 minutes
Bias-Variance Trade-off 6 minutes
Assessing Accuracy — Classification 4 minutes
Bayes Classifier Part I 7 minutes
Bayes Classifier Part II 3 minutes
Assessing Accuracy — KNN 7 minutes

2 programming assignments Total 360 minutes

Statistical Learning 180 minutes
Quiz 1 — Statistical Learning 180 minutes

1 discussion prompt Total 10 minutes

Training Error Rate and Testing Error Rate Analogy 10 minutes

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

6 videos Total 35 minutes

Multiple Linear Regression Overview 4 minutes
Relationship Between X and Y 11 minutes
Qualitative Predictors 5 minutes
Interaction Terms 4 minutes
Multicollinearity 6 minutes
Linear Regression vs. KNN Regression 5 minutes

3 programming assignments Total 540 minutes

Linear regression 180 minutes
Linear Regression Using Tidy Models 180 minutes
Quiz 2 — Linear Regression 180 minutes

What's included

7 videos1 discussion prompt

What's included

8 videos5 programming assignments

8 videos Total 51 minutes

LDA 7 minutes
LDA Estimates 6 minutes
LDA with p > 1 7 minutes
Standard to Multivariate Details 7 minutes
QDA 6 minutes
Naive Bayes 5 minutes
Poisson Regression 6 minutes
Link Functions and Conclusion 8 minutes

5 programming assignments Total 900 minutes

Classification 180 minutes
Classification Part 2 180 minutes
Classification Using Tidy Models 180 minutes
Classification Using Tidy Models Part 2 180 minutes
Quiz 3 — Classification 180 minutes

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

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