LINEAR REGRESSION & GRADIENT DESCENT | Machine Learning Practices | Session - 9

LINEAR REGRESSION & GRADIENT DESCENT | Machine Learning Practices | Session - 9
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In this session, we've covered:-
- Linear Regression (Explanation)
- Linear Regression Implementation from Scratch
- Cost Function - Mean Squared Error
- Gradient Descent
- Regularization: Ridge, Lasso
Kaggle notebook 🔼 (Make sure to upvote it):
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Chapters:
00:00 Intro
02:50 ML Algorithms & Techniques
06:44 Intuition - Linear Regression
15:55 Formula - Linear Regression
17:29 Notations - Linear Regression
20:13 How Slope works
21:21 How Intercept works
25:33 Interactive Visualization for W & B
27:45 Mean Squared Error - Cost Function
40:24 Interactive Visualizations for MSE/MAE
43:33 Gradient Descent Intuition
56:10 Gradient Descent Algorithm
58:00 3D Visualization of Cost Function
01:48:31 Linear Regression Implementation
01:11:11 Cost Function MSE Implementation
01:16:42 Learning Rate α
01:22:23 Gradients Implementation
01:31:10 Epochs/Iterations/Steps
01:31:46 Gradient Descent Implementation
01:41:25 Running Gradient Descent
01:43:30 Plotting Regression line
01:49:40 Tweaking Epochs & Alpha
01:57:00 Regularization
01:57:16 (L1) Lasso Regularization
01:59:13 (L2) Ridge Regularization
02:00:40 Linear Regression variants in Scikit-learn
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