# Regression and Classification

Join the Regression and Classification course to explore statistical modeling, choosing appropriate models, and supervised vs. unsupervised techniques. This interdisciplinary degree program offered by University of Colorado Boulder is designed for individuals seeking to understand the importance and practical use of statistical learning in data science applications.

• Gain insights into when to use specific models, how to tune them, and the trade-offs involved.
• Learn to identify the strengths, weaknesses, and caveats of different models for statistical problem-solving.
• Understand the distinctions between supervised and unsupervised techniques and apply them appropriately to different types of data and problems.

Certificate Available ✔

##### Course Modules

This course offers a comprehensive exploration of statistical learning, covering topics such as statistical modeling, supervised vs. unsupervised techniques, regression, classification, and more. Ideal for individuals with varied backgrounds in computer science, information science, mathematics, and statistics.

#### Statistical Learning Introduction

Module 1: Statistical Learning Introduction

• Explore the basics of statistical learning and understand the distinctions between supervised and unsupervised techniques.
• Learn about notation, prediction, inference, parametric methods, and the trade-offs between interpretability and flexibility.
• Discover the academic credit opportunities and available course support.

#### Accuracy

Module 2: Accuracy

• Delve into model accuracy and the bias-variance trade-off, and learn how to assess accuracy in classification and KNN.
• Understand the analogy between training error rate and testing error rate.

#### Simple Linear Regression

Module 3: Simple Linear Regression

• Gain insights into coefficient estimation, accuracy of coefficient estimates, and correlation in simple linear regression.
• Tackle the challenges related to correlation problems.

#### Multiple Linear Regression

Module 4: Multiple Linear Regression

• Explore the relationship between X and Y, qualitative predictors, interaction terms, and multicollinearity in multiple linear regression.
• Understand the differences between linear regression and KNN regression.

#### Classification Overview

Module 5: Classification Overview

• Learn about the overview of classification, linear vs. logistic regression, logistic regression, and estimating coefficients.
• Explore multiple logistic regression and generative models.

#### Classification Models

Module 6: Classification Models

• Discover LDA, QDA, naive Bayes, and Poisson regression in the context of classification.
• Get insights into link functions and the use of linear regression in classification.

#### Introduction to Computational Statistics for Data Scientists

Databricks

This course provides an introduction to computational statistics for data scientists, focusing on Bayesian inference using Python and PyMC3 for real-world scenarios....

#### Bayesian Statistics: Time Series Analysis

University of California, Santa Cruz

Bayesian Statistics: Time Series Analysis provides a comprehensive exploration of building models, using R for analysis and forecasting, and explaining stationary...

#### Inferential Statistics

Duke University

Inferential Statistics offers a comprehensive exploration of statistical inference methods for both numerical and categorical data, emphasizing practical applications...

#### Model Diagnostics and Remedial Measures

Illinois Tech

Model Diagnostics and Remedial Measures is a comprehensive course focusing on detecting and remedying violations of linear regression model assumptions. Participants...