Logistic Regression: Fundamentals and Applications

This lecture covers the fundamentals of logistic regression, including linear class, stochastic gradient descent, cross entropy cost, regularization, classification vs regression, decision boundaries, and real-world examples using scikit-learn. The instructor explains the concepts with practical examples and discusses the importance of precision, sensitivity, specificity, and accuracy in classification tasks.