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Python and ML proficiency
·
Module 10
Supervised Learning: Regression
1
Implementing Linear Regression from Scratch
Implement simple linear regression from scratch
2
Multiple Linear Regression
Extend linear regression to multiple features
3
Polynomial Regression for Non-Linear Data
Apply polynomial regression for non-linear relationships
4
Regularization: Ridge and Lasso
Use regularization techniques (Ridge, Lasso) to prevent overfitting
5
Regression Models with scikit-learn
Train and evaluate regression models using scikit-learn
6
Interpreting Regression Coefficients
Interpret regression coefficients to explain feature-target relationships
7
Evaluating Regression Model Fit
Assess regression model fit using R-squared, MSE, and residual analysis
Previous module
Introduction to Machine Learning
Next module
Supervised Learning: Classification