Sign inStart free
AI Engineering/Phase 01/Lesson 15

Statistics for Machine Learning

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

Build 2 hr Python

Prerequisites: Phase 1, Lessons 06 (Probability and Distributions), 07 (Bayes' Theorem)

#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
Full Access lesson

Keep going. The rest of this lesson is one step away.

Unlock all 503 lessons, video, code, quizzes and every future update. Pay in your own currency with Paystack.

See plans Sign in

The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.