Mathematics

Learn the mathematics behind data, machine learning, and everyday decisions in simple English.

Probability learning path

Probability Foundations — outcomes, events, complements, unions, and the main rules.
Counting for Probability — permutations and combinations: count possibilities without listing them.
Conditional Probability — “given that” questions, including a complete Python problem.
Independence and Bayes’ Theorem — update a belief when new evidence arrives.
Random Variables and Distributions — expected value, variance, and common distributions.
Advanced Probability — total probability, joint distributions, covariance, LLN, and CLT.

Every lesson uses this format:

Learn probability from zero using simple questions, formulas, plain-English explanations, and worked answers.

Count possible outcomes using the multiplication rule, permutations, and combinations before calculating probability.

Learn conditional probability using simple class examples, complete formulas, input validation, and a Python solution.

Learn whether events affect each other and use Bayes' theorem to update probability after new evidence.

Understand random variables, probability distributions, expected value, variance, and standard deviation through simple questions.

Connect probability ideas using joint distributions, covariance, conditional expectation, the law of large numbers, and the central limit theorem.