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Full notes at ocdevel.com/mlg/7. See Andrew Ng Week 3 Lecture Notes

Overview

• Logistic Function: A sigmoid function transforming linear regression output to logits, providing a probability between 0 and 1.
• Binary Classification: Logistic regression deals with binary outcomes, determining either 0 or 1 based on a threshold (e.g., 0.5).
• Error Function: Uses log likelihood to measure the accuracy of predictions in logistic regression.
• Gradient Descent: Optimizes the model by adjusting weights to minimize the error function.

Classification vs Regression

• Classification: Predicts a discrete label (e.g., a cat or dog).
• Regression: Predicts a continuous outcome (e.g., house price).

Practical Example

• Train on a dataset of house features to predict if a house is 'expensive' based on labeled data.
• Automatically categorize into 0 (not expensive) or 1 (expensive) through training and gradient descent.

Logistic Regression in Machine Learning

• Neurons in Neural Networks: Act as building blocks, as logistic regression is used to create neurons for more complex models like neural networks.
• Composable Functions: Demonstrates the compositional nature of machine learning algorithms where functions are built on other functions (e.g., logistic built on linear).