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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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
- Statistical Methods
- Predictive Modeling
- Statistical Analysis
- Classification And Regression Tree (CART)
- Data Science
- Model Evaluation
- Statistical Machine Learning
- Statistical Modeling
- Model Training
- Regression Analysis
- Unsupervised Learning
- Statistical Inference
- Machine Learning Algorithms
- Machine Learning
- Supervised Learning
- Logistic Regression
Tools you'll learn
Details to know
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- 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
5 videos•Total 30 minutes
- Simple Linear Regression Overview•6 minutes
- Coefficient Estimation•9 minutes
- Accuracy of Coefficient Estimates•6 minutes
- Model Accuracy•6 minutes
- Correlation•4 minutes
1 discussion prompt•Total 10 minutes
- Correlation Problem•10 minutes
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
7 videos•Total 42 minutes
- Classification Overview•6 minutes
- Linear vs. Logistics Regression•7 minutes
- Logistic Regression•3 minutes
- Estimating Coefficients•4 minutes
- Multiple Logistic Regression•10 minutes
- Generative Models Part I•4 minutes
- Generative Models Part II•7 minutes
1 discussion prompt•Total 10 minutes
- Use of Linear Regression in Classification•10 minutes
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
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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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Reviewed on Apr 28, 2024
Great course with clear and concise explanation. I highly recommend taking the course.
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