Phase 2: Probability & Statistics · ~120 minutes · Python

Statistics for Machine Learning

Statistics is how you know if your model actually works or just got lucky.

What you will learn

Compute descriptive statistics, Pearson/Spearman correlation, and covariance matrices from scratch
Perform hypothesis tests (t-test, chi-squared) and interpret p-values and confidence intervals correctly
Use bootstrap resampling to construct confidence intervals for any metric without distributional assumptions
Distinguish statistical significance from practical significance using effect size measures

Introduction

Type: Build
Language: Python
Prerequisites: Probability and Distributions, Bayes' Theorem
Time: ~120 minutes

Objective

Learning objectives

Compute descriptive statistics, Pearson/Spearman correlation, and covariance matrices from scratch
Perform hypothesis tests (t-test, chi-squared) and interpret p-values and confidence intervals correctly
Use bootstrap resampling to construct confidence intervals for any metric without distributional assumptions
Distinguish statistical significance from practical significance using effect size measures

Unlock the full lesson

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

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