Regression and Classification
Master statistical learning techniques for data analysis with hands-on practice in R. From linear regression to classification models.
Master statistical learning techniques for data analysis with hands-on practice in R. From linear regression to classification models.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Statistical Learning for Data Science Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
3.9
(12 ratings)
2,437 already enrolled
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What you'll learn
Master statistical learning fundamentals and their applications
Develop skills in supervised and unsupervised learning techniques
Gain proficiency in regression and classification methods
Learn to assess and select appropriate models
Understand the bias-variance trade-off in statistical learning
Apply statistical learning techniques using R programming
Skills you'll gain
This course includes:
3.8 Hours PreRecorded video
2 programming assignments, 1 discussion prompt
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This comprehensive course explores statistical learning concepts essential for data science and machine learning. Beginning with foundational principles of supervised and unsupervised learning, students progress through regression techniques, classification methods, and model assessment. The curriculum emphasizes both theoretical understanding and practical application, featuring hands-on programming assignments in R. Topics include linear regression, logistic regression, LDA, QDA, and model selection techniques.
Statistical Learning Introduction
Module 1 · 1 Hours to complete
Accuracy
Module 2 · 6 Hours to complete
Simple Linear Regression
Module 3 · 40 Minutes to complete
Multiple Linear Regression
Module 4 · 9 Hours to complete
Classification Overview
Module 5 · 51 Minutes to complete
Classification Models
Module 6 · 15 Hours to complete
Fee Structure
Instructor
Instructor
Dr. James Bird is an Instructor at the University of Colorado Boulder, specializing in data science and applied mathematics. He holds a Ph.D. in Computer Science from the University of California, Santa Barbara, along with an M.S. in Statistics and an M.A. in Applied Physics/Applied Mathematics. His extensive academic background is complemented by a B.A. in Mathematics, providing him with a solid foundation in quantitative analysis.At CU Boulder, Dr. Bird teaches several courses that are integral to the data science curriculum, including "Essential Linear Algebra for Data Science," "Integral Calculus and Numerical Analysis for Data Science," and "Regression and Classification." His research interests focus on the implementation of artificial intelligence for deep space travel, reflecting his commitment to advancing knowledge in both theoretical and practical aspects of data science. Additionally, he has worked as a Statistician with Gallup since 2015, further enhancing his expertise in statistical methods and data analysis. Through his teaching and research, Dr. Bird plays a vital role in preparing students for successful careers in data science and analytics.
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3.9 course rating
12 ratings
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