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
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Regression and Classification
This course is part of Statistical Learning for Data Science Specialization
Instructor: James Bird
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Intermediate level
Recommended experience
Flexible schedule
4 weeks at 10 hours a week
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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
- Machine Learning
- Model Training
- Predictive Modeling
- Statistical Methods
- Machine Learning Algorithms
- Data Science
- Supervised Learning
- Classification And Regression Tree (CART)
- Statistical Analysis
- Statistical Inference
- Regression Analysis
- Logistic Regression
- Statistical Machine Learning
- Model Evaluation
- Statistical Modeling
- Unsupervised Learning
Tools you'll learn
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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.
- 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
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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
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