WEEK 3
Logistic Regression
Logistic regression is a method for classifying data into discrete outcomes. For example, we might use logistic regression to classify an email as spam or not spam. In this module, we introduce the notion of classification, the cost function for logistic regression, and the application of logistic regression to multi-class classification.
7 videos, 8 readings
Video: Classification
Reading: Classification
Video: Hypothesis Representation
Reading: Hypothesis Representation
Video: Decision Boundary
Reading: Decision Boundary
Video: Cost Function
Reading: Cost Function
Video: Simplified Cost Function and Gradient Descent
Reading: Simplified Cost Function and Gradient Descent
Video: Advanced Optimization
Reading: Advanced Optimization
Video: Multiclass Classification: One-vs-all
Reading: Multiclass Classification: One-vs-all
Reading: Lecture Slides
Graded: Logistic Regression
Regularization
Machine learning models need to generalize well to new examples that the model has not seen in practice. In this module, we introduce regularization, which helps prevent models from overfitting the training data.
4 videos, 5 readings
Video: The Problem of Overfitting
Reading: The Problem of Overfitting
Video: Cost Function
Reading: Cost Function
Video: Regularized Linear Regression
Reading: Regularized Linear Regression
Video: Regularized Logistic Regression
Reading: Regularized Logistic Regression
Reading: Lecture Slides
Programming: Logistic Regression
Graded: Regularization