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Regression and Classification
  • Level Professional
  • Duration 35 hours
  • Course by University of Colorado Boulder
  • Offered by

About

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.

Modules

Introduction

    • 2 Readings
    • 1 Discussion
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1 Discussions

  • Introduce Yourself

2 Readings

  • Earn Academic Credit for your Work!
  • Course Support

Setting the Foundation

    • 4 Videos
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4 Videos

  • Introduction and Welcome
  • Supervised vs. Unsupervised
  • Notation Overview
  • Overview Example & Discussion

General Concepts

    • 5 Videos
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5 Videos

  • Prediction
  • Inference
  • Parametric Methods
  • Interpretability vs. Flexibility
  • Quantitative vs. Qualitative

Accuracy

    • 3 Videos
    • 1 Discussion
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1 Discussions

  • Training Error Rate and Testing Error Rate Analogy

3 Videos

  • Model Accuracy
  • Bias-Variance Trade-off
  • Assessing Accuracy — Classification

Bayes Classifier

    • 3 Videos
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3 Videos

  • Bayes Classifier Part I
  • Bayes Classifier Part II
  • Assessing Accuracy — KNN

Assessments

    • 2 Programming
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2 Programming

  • Statistical Learning
  • Quiz 1 — Statistical Learning

Coefficients

    • 3 Videos
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3 Videos

  • Simple Linear Regression Overview
  • Coefficient Estimation
  • Accuracy of Coefficient Estimates

Concepts

    • 2 Videos
    • 1 Discussion
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1 Discussions

  • Correlation Problem

2 Videos

  • Model Accuracy
  • Correlation

Multiple Linear Regression

    • 2 Videos
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2 Videos

  • Multiple Linear Regression Overview
  • Relationship Between X and Y

Predictors

    • 2 Videos
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2 Videos

  • Qualitative Predictors
  • Interaction Terms

Model Specifics

    • 2 Videos
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2 Videos

  • Multicollinearity
  • Linear Regression vs. KNN Regression

Assessments

    • 3 Programming
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3 Programming

  • Linear regression
  • Linear Regression Using Tidy Models
  • Quiz 2 — Linear Regression

Logistic Regression

    • 3 Videos
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3 Videos

  • Classification Overview
  • Linear vs. Logistics Regression
  • Logistic Regression

Logistic Regression Part II

    • 2 Videos
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2 Videos

  • Estimating Coefficients
  • Multiple Logistic Regression

Generative Models

    • 2 Videos
    • 1 Discussion
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1 Discussions

  • Use of Linear Regression in Classification

2 Videos

  • Generative Models Part I
  • Generative Models Part II

LDA

    • 4 Videos
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4 Videos

  • LDA
  • LDA Estimates
  • LDA with p > 1
  • Standard to Multivariate Details

QDA & Naive Bayes

    • 2 Videos
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2 Videos

  • QDA
  • Naive Bayes

Poisson Regression, Link Functions & Conclusion

    • 2 Videos
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2 Videos

  • Poisson Regression
  • Link Functions and Conclusion

Assessments

    • 5 Programming
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5 Programming

  • Classification
  • Classification Part 2
  • Classification Using Tidy Models
  • Classification Using Tidy Models Part 2
  • Quiz 3 — Classification

Auto Summary

Unlock the power of statistical modeling with the "Regression and Classification" course, a comprehensive exploration into the world of Maths and Statistics. Guided by expert instructors from Coursera, this professional-level course is designed to enhance your understanding of when and how to use various statistical models effectively. Key topics include Regression, Classification, Trees, Resampling, and Unsupervised techniques, among others. This course is part of the University of Colorado Boulder's Master of Science in Data Science (MS-DS) degree, available on the Coursera platform. The MS-DS program is unique for its interdisciplinary approach, featuring faculty from departments such as Applied Mathematics, Computer Science, and Information Science. With a flexible, performance-based admissions process that requires no formal application, the MS-DS degree caters to individuals with diverse educational and professional backgrounds in areas like computer science, information science, mathematics, and statistics. With a total duration of 35 hours, you can dive deep into statistical learning at your own pace. Subscription options start with the Starter plan, making it accessible for those looking to advance their skills in data science. Ideal for professionals seeking to enhance their expertise or pivot their careers, this course offers valuable insights and practical knowledge to apply in real-world scenarios. Embark on your learning journey today and take a significant step towards mastering statistical modeling and data science.

James Bird
Instructor

James Bird