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Module 8
Supervised Learning: Regression Models
1
Understanding Linear Regression: Concepts and Assumptions
Explain the concept of linear regression and its underlying assumptions
2
Mean Squared Error: The Cost Function
Derive the cost function (Mean Squared Error) for linear regression
3
Implementing Linear Regression with NumPy: Prediction and Cost Functions
Implement the prediction and cost functions for linear regression using NumPy
4
Gradient Descent for Linear Regression
Implement gradient descent to train a simple linear regression model from scratch
5
Linear Regression with scikit-learn
Fit and interpret a linear regression model using scikit-learn
6
Polynomial Regression for Non-Linear Data
Explain how polynomial regression can capture non-linear relationships
7
Evaluating Regression Models with MSE and R-squared
Evaluate regression models using metrics like Mean Squared Error (MSE) and R-squared
Previous module
Foundations of Machine Learning
Next module
Supervised Learning: Classification Models