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ML_Supervised
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Module 4
Logistic Regression from Scratch
1
Implementing and Visualizing the Sigmoid Function
Implement the sigmoid function and visualize its output.
2
Gradient of Binary Cross-Entropy Loss
Derive and calculate the gradient of the binary cross-entropy loss function.
3
Building Logistic Regression with Gradient Descent
Implement binary logistic regression from scratch using your gradient descent optimizer.
Visualizing Logistic Regression Decision Boundaries
Visualize the decision boundary of a trained logistic regression model on a 2D dataset.
5
Implementing One-vs-All for Multi-Class Classification
Implement the one-vs-all strategy on top of your binary classifier for multi-class problems.
6
Softmax and Categorical Cross-Entropy
Implement the softmax function and calculate the categorical cross-entropy loss.
7
Softmax Regression for Multi-Class Classification
Implement softmax (multinomial logistic) regression for multi-class classification.
Previous module
Linear Regression from Scratch
Next module
Regularization Techniques