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ML_Supervised
·
Module 3
Linear Regression from Scratch
1
MSE Gradient for Linear Regression
Derive and calculate the gradient of the Mean Squared Error (MSE) loss function for linear regression.
2
Vectorized Linear Regression with NumPy
Implement a vectorized version of linear regression using gradient descent and NumPy.
3
Standard Scaling for Faster Convergence
Implement feature normalization (standard scaling) and demonstrate its impact on convergence speed.
4
Normal Equation for Linear Regression
Implement the normal equation to solve for linear regression coefficients directly.
5
Gradient Descent vs. Normal Equation: A Trade-off Analysis
Compare the computational and practical trade-offs between gradient descent and the normal equation.
Previous module
Optimization with Gradient Descent
Next module
Logistic Regression from Scratch