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Probability & Statistics for ML

Probability distributions, Bayes theorem, and statistical thinking for machine learning.

6 lessons ~3 hours 370 XP

Chapter 1: Probability Foundations

Basic probability, conditional probability, and Bayes theorem.

1.1

Basic Probability Rules

The foundation of probabilistic ML.

Soon+50 XP
1.2

Conditional Probability

When knowing one thing changes the probability of another.

Soon+60 XP
1.3

Bayes Theorem

The foundation of Bayesian machine learning.

Soon+70 XP

Chapter 2: Probability Distributions

Normal, binomial, Poisson — the shapes of randomness.

2.1

Common Probability Distributions

Normal, binomial, Bernoulli, Poisson, uniform.

Soon+60 XP
2.2

Expected Value and Variance

Summarizing distributions with numbers.

Soon+60 XP
2.3

Maximum Likelihood Estimation

How models learn their parameters.

Soon+70 XP