Analytics and AI: Modern Regression and AI

  • Overview
  • Course Content
  • Requirements & Materials
Overview

Analytics and AI: Modern Regression and AI

Course Description

This course introduces regression modeling, the foundation of many other AI/machine learning models through recorded lectures that participants can watch each week on their own schedule. The online modules cover simple/multiple linear regression, generalized linear models like logistic and Poisson regression, and model selection. These fundamentals are reinforced through implementation with data examples, using the Python programming language and R statistical software. Over the course of the semester, learners will gain the skills to answer analysis/prediction questions by selecting the appropriate regression tools, implementing the models using the appropriate libraries, interpreting the results within the context of the data/question, and communicating their results to both technical and non-technical audiences.

Course Content

Simple Linear Regression and ANOVA

  • Basics and model estimation
  • Statistical inference and prediction
  • Assumptions and diagnostics
  • Case studies using real data examples

Multiple Linear Regression

  • Model estimation, statistical inference and prediction
  • Goodness of fit and model performance
  • Case study: application of multiple regression in the presence of bias selection
  • Case study: application of multiple regression for large sample size data

Generalized Linear Regression

  • Logistic regression: estimation, inference, and prediction
  • Classification of binary data using machine learning modeling
  • Poisson regression: estimation, inference, and prediction
  • Case studies using real data examples

Variable Selection

  • Fundamentals of model selection
  • Stepwise regression
  • Regularized regression: ridge, LASSO (Least Absolute Shrinkage and Selection Operator), and elastic net
  • Case studies using real data examples

 

Requirements & Materials

Prerequisites

RECOMMENDED:

  • Familiarity with R statistical programming language

REQUIRED:

  • Undergraduate-level statistics and probability, along with basic linear algebra and calculus experience
  • Programming proficiency in Python

Materials

REQUIRED (Student must provide):

  • Internet connection
  • Free software (download and install before taking the course)
    • R statistical software (see cran.r-project.org) and R Studio (see rstudio.com/products/rstudio/download)
    • Python programming language (see www.python.org)
    • Jupyter Notebooks (see jupyter.org)
    • GitHub account (see github.com)
    • Adobe Acrobat PDF reader (see get.adobe.com/reader/)
    • Honorlock proctoring software (see honorlock.com/)
  • Laptop or desktop computer (not a tablet)

PROVIDED (Student will receive):

  • All course lessons (video recorded lectures, slides, and transcripts), assignments, and solutions
  • R and Python Jupyter notebooks with course examples
  • GitHub repository of R and Python code for examples

Who Should Attend

This course is designed for anyone who wants to use fundamental AI techniques to analyze data with multiple features, build predictive models, and make data-driven decisions using regression methods. It is also ideal for anyone who wants a strong foundation to build on when learning other AI models for analysis and prediction.

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What You Will Learn

  • Construction and estimation of regression models using real or simulated data
  • Implementation of regression models using libraries in the Python programming language and R statistical programming language
  • Interpretion of regression outputs including effects, residuals, and significance measures
  • Diagnosis of a model’s goodness of fit, multicollinearity, and influential points and outliers
  • Comparison of models using criteria like adjusted R2, AIC, and cross-validation
  • How to perform feature selection, and understand and interpret its outputs and appropriate usage
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How You Will Benefit

  • Gain foundational AI knowledge of regression techniques.
  • Develop the ability to build and interpret regression models.
  • Strengthen AI-based decision-making skills using data-driven insights.
  • Apply regression tools using libraries in Python and R.
  • Communicate regression/AI findings clearly to diverse audiences.
  • Enhance your qualifications for data-centric roles across industries.
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