Phase 1: Classification Algorithms · ~90 minutes · Python

Logistic Regression

Logistic regression bends a straight line into an S-curve to answer yes-or-no questions with probabilities.

What you will learn

Implement logistic regression from scratch using the sigmoid function and binary cross-entropy loss
Compute and interpret precision, recall, F1 score, and the confusion matrix for binary classification
Explain why MSE fails for classification and why binary cross-entropy produces a convex cost surface
Build a softmax regression model for multi-class classification and evaluate threshold tuning tradeoffs

Introduction

Type: Build
Languages: Python
Prerequisites: Linear Regression
Time: ~90 minutes

Objective

Learning objectives

Implement logistic regression from scratch using the sigmoid function and binary cross-entropy loss
Compute and interpret precision, recall, F1 score, and the confusion matrix for binary classification
Explain why MSE fails for classification and why binary cross-entropy produces a convex cost surface
Build a softmax regression model for multi-class classification and evaluate threshold tuning tradeoffs

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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