Linear Regression and Regularisation
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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