Linear-Regression-Step-by-Step

"Linear Regression Step by Step" is a repository that provides a comprehensive notebook with step-by-step examples, exercises and libraries to understand and implement Linear Regression easily.

Learning Agenda of Notebook

• ML Overview
  • Example, Algorithms vs Model
• Supervised Learning
  • Definition, Examples
• Supervised Learning Setup
  • Nomenclature, Formulation(Regression & Classification), Example, Learning, Hypothesis Class.
  • Performance Evaluation
    • Loss Function, 0/1 Loss Function, Squared Loss, Root Mean squared error, Absolute Loss
  • Generalization: The Train-Test Split, Generalization loss.

Linear Regression

• Single Feature
• Multiple Feature
• Model Formulation and Setup
• Loss Function
  • How to solve?
  • Reformulation
  • Consequently
• Solve Optimization Problem (Analytical Solution employing Calculus)
• Model Evaluation Techniques
• Polynomial Regression
• How to Handle Overfitting?
• Regularization (Ridge Regression and Lasso Regression)
• Gradient Descent Algorithm
  • Formulation
  • Algorithm
  • Types
• Linear Regression Implementation in Python
• Linear Regression Implementation using sklearn
• Project: Medical Insurance Cost Prediction
• Interview Questions