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SECTION 1: Introduction to Linear Regression
What is Linear Regression
How to Get Queries Resolved
Download Resources
Regression Equation and Terminologies
Time Series Regression
Formula for Coefficients
Coefficients Computation from Scratch
SECTION 2: Regression Algorithm from scratch: Gradient Descent
What is Gradient Descent
Math behind Gradient Descent
Stochastic and Minibatch
Gradient Descent from Scratch - Coding
Course Review
SECTION 3: Model Building and Concepts Part- 1
Problem Description and Data
Describe the Data
EDA Part 1 Understanding Sales
EDA Part 2 Graphical Analysis
Missing Values and Outlier
Outliers and mahalanobis distance
Building and Interpreting Linear Regression
R-Squared Intuition
Adjusted R Squared and F Statistic
SECTION 4: Model Building and Concepts Part- 2
Assumptions of OLS - Part 1
Assumptions of OLS - Part 2
Assumptions of OLS - Part 3
VIF
Durbin Watson statistic and Condition Number
Multicollinearity
How to check and rectify Heteroscedasticity
BoxCox Transform
The Other Way
Model Improvement Tactic Demo- Cooks D
SECTION 5: Model Selection Approaches
Evaluating Regression Models
Cross Validation Approaches
Need for Holdout Sample
Tactic 1 - LR Model Building
Tactic 2 - Backward Building Workout
Stepwise and Best Subsets
RFE and Caveats
RFE Demo
SECTION 6: Regularization Modelling Approaches
Bias Variance Tradeoff
Ridge Regression
Grid Search
LASSO Regression
ElasticNet
Ridge Regularization Code Demo
Why weights of L1 regularization reach zero but not L2
SECTION 7: Outlier Resistant and Advanced Regression
RANSAC for Outliers Resistant Models
Theil-Sen Regression
Time Series Regression
Retraining Models
How to Identify if Data Drift occurred
Quantile Regression
Robust Regression with Huber Loss
Advanced Regression Code
SECTION 8: Maximum Estimation for Linear Regression
Linear Regression with Maximum Likelihood Estimation
Assignment