Meaning of 'Linear'
Meaning of ‘Linear’ in Linear Regression
Linear Regression
Linear Regression
less than a minute
This course includes
13 articles
30 mins reading time
16 videos • 4 hr 26 mins
Course content
13 sections • 83 topics • Read: 30 mins • Video: 4 hr 26 mins
Meaning of 'Linear'
Polynomial Features ✅
Non-Linear Features ✅
Non-Linear Parameters ❌
Importance of Linearity
Why Linear Regression is NOT 'Linear' ? | Meaning of 'Linear' in Linear Regression | Explained
Meaning of 'Regression'
Why Linear Regression is NOT 'Regression' ? | Meaning of 'Regression' in Linear Regression
Linear Regression
Predict Salary
Parameters/Weights of the Model
Loss Function
Issues with Absolute Value function
Cost Function
Optimization
Find the Minima
Matrix Form of Cost Function
Normal Equation
Issues with Normal Equation
Pseudo Inverse
Linear Regression | Dimensions of Fitting Hyperplane | Explained with Example
Parameters of Linear Regression | Issues with Absolute Error | Loss Function | Cost Function
Normal Equation | Issues with Inverse | Pseudo Inverse | Explained with Example
Probabilistic Interpretation
Probabilistic Interpretation
Model Assumptions
Error Assumptions
Probability Vs Likelihood
Maximum Likelihood Estimate (MLE)
Issue with Likelihood
Solution: Log Likelihood
Ordinary Least Squares
Probabilistic Interpretation of Linear Regression | MLE | Probability vs Likelihood | Explained
Convex Function
Convexity
Is MSE Cost function Convex ? YES ✅
MSE: Positive Semi Definite (Proof)
Convex Function | Why Mean Squared Error Function is Convex ? | Explained
Gradient Descent
Goal
Issues with Normal Equation
Gradient Descent
Algorithm
Gradient Calculation
Learning Rate
Learning Rate Techniques
Types of Gradient Descent
Batch Gradient Descent (BGD)
Stochastic Gradient Descent (SGD)
Mini Batch Gradient Descent
BGD vs SGD vs Mini-BGD
Gradient Descent Algorithm | Learning Rate | MSE Gradient Calculation | Explained with Examples
Types of Gradient Descent | Batch GD | Stochastic GD | Mini-Batch GD | Explained with Examples
Polynomial Regression
Polynomial Regression
Strategy to find Polynomial Features
High Degree Polynomial Regression
Conclusion
Polynomial Regression | Polynomial Features | Overfitting | Underfitting | Explained with Examples
Data Splitting
Train/Validation/Test Split
Data Leakage
Typical Split Ratios
Imbalanced Data
Stratified Sampling
Time-Series Data
Data Splitting (Train/Validation/Test Split) | Data Leakage | Stratified Sampling | Time Series Data
Cross Validation
Core Idea
Cross-validation
K-Fold Cross-Validation
Leave-One-Out Cross-Validation (LOOCV)
Cross Validation | K-Fold CV | LOOCV in Machine Learning | Explained with Examples
Bias Variance Tradeoff
Bias-Variance Decomposition
Bias
Variance
Linear (High Bias), Polynomial(High Variance)
Bias Variance Table
Bias-Variance Trade-Off
Fix High Bias (Under-Fitting)
Fix High Variance (Over-Fitting)
Bias Variance TradeOff in Machine Learning | Underfitting vs Overfitting | Explained with Examples
Regularization
Over-Fitting
How to Avoid Over-Fitting ?
Regularization
Regularization introduces Bias
Common Regularization Techniques
L2 Regularization
L1 Regularization
Elastic Net Regularization
L1/L2/Elastic Net Regularization Comparison
L1 vs L2 Regularization Comparison
Regularization (L1/L2/ElasticNet) | Why weights shrink to 0 in L1 Regularization ? | Explained
Regression Metrics
Regression Metrics
Mean Absolute Error(MAE)
Mean Squared Error(MSE)
Root Mean Squared Error(RMSE)
R^2 Metric
Huber Loss
Linear Regression Metrics | MAE | MSE | RMSE | R² Metric | Huber Loss | Explained with Examples
Assumptions
Assumptions
Linearity
Independence of Errors (No Auto-Correlation)
Homoscedasticity
Normality of Errors
No Multicollinearity
No Endogeneity (Exogeneity)
Assumptions of Linear Regression | Linearity | Normality | Homoscedasticity | Multicollinearity
Meaning of 'Regression'
Meaning of ‘Regression’ in Linear Regression
Linear Regression
Linear Regression
Probabilistic Interpretation
Probabilistic Interpretation of Linear Regression
Convex Function
Convex Function
Gradient Descent
Gradient Descent
Polynomial Regression
Polynomial Regression
Data Splitting
Data Splitting
Cross Validation
Cross Validation
Bias Variance Tradeoff
Bias Variance Tradeoff
Regularization
Regularization
Regression Metrics
Regression Metrics
Assumptions
Assumptions Of Linear Regression