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.

Hiring signal: Understanding of logistic regression internals

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: Phase 2 Lesson 1-2 (What Is ML, Linear Regression) Time: ~90 minutes

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

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

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