University of Colorado Boulder - Regression and Classification
- Offered byCoursera
Regression and Classification at Coursera Overview
Duration | 35 hours |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Regression and Classification at Coursera Highlights
- Reset flexible deadlines in accordance to your schedule
Earn a Certificate upon completion
Regression and Classification at Coursera Course details
- 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
Regression and Classification at Coursera Curriculum
Statistical Learning Introduction
Introduction and Welcome
Supervised vs. Unsupervised
Notation Overview
Overview Example & Discussion
Prediction
Inference
Parametric Methods
Interpretability vs. Flexibility
Quantitative vs. Qualitative
Welcome and Where to Find Help
Accuracy
Model Accuracy
Bias-Variance Trade-off
Assessing Accuracy -Classification
Bayes Classifier Part I
Bayes Classifier Part II
Assessing Accuracy -KNN
Simple Linear Regression
Simple Linear Regression Overview
Coefficient Estimation
Accuracy of Coefficient Estimates
Model Accuracy
Correlation
Multiple Linear Regression
Multiple Linear Regression Overview
Relationship Between X and Y
Qualitative Predictors
Interaction Terms
Multicollinearity
Linear Regression vs. KNN Regression
Classification Overview
Classification Overview
Linear vs. Logistics Regression
Logistic Regression
Estimating Coefficients
Multiple Logistic Regression
Generative Models Part I
Generative Models Part II
Classification Models
LDA
LDA Estimates
LDA with p > 1
Standard to Multivariate Details
QDA
Naive Bayes
Poisson Regression
Link Functions and Conclusion
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