Course curriculum

  • 1

    Linear Regression

    • Introduction to Linear Regression

    • History and Motivation

    • Ordinary Least Squares Theory

    • Cost Function Theory

    • Gradient Descent Theory

    • Coding Simply Linear Regression

    • Scikit-Learn Overview

    • Linear Regression with Scikit-Learn - Train | Test Splits and Training

    • Linear Regression with Scikit-Learn - Performance Evaluation

    • Linear Regression with Scikit-Learn - Residual Plots

    • Linear Regression with Scikit-Learn - Coefficients and Deployment

  • 2

    Polynomial Regression

    • Polynomial Regression - Motivation

    • Polynomial Regression - Creating Polynomial Feature Set

    • Polynomial Regression - Training and Evaluating Performance

    • Bias Variance Trade-Off

    • Polynomial Regression- Choosing Polynomial Order

    • Polynomial Regression - Model Deployment

  • 3

    Regularization Methods (Ridge, Lasso, Elastic Net)

    • Regularization Overview

    • Feature Scaling

    • Cross Validation

    • Regularization - Data Setup

    • Ridge Regression - Theory

    • Ridge Regression - Implementation with Python and Scikit-Learn

    • Lasso Regression

    • Elastic Net

  • 4

    Overview of Data Set Used in Next ML Sections

    • Data Set Overview