Probability

Uncertainty made precise: random variables, distributions, Bayes' theorem and the laws behind every ML model.

intermediate~30 hours10 topics~18h of lessonsRoadmap Level 5

CS proofs, algorithms and ML are all math under the hood; the notation pays off everywhere.

RECOMMENDED BEFORE THIS
○ Discrete Mathematics

These are suggested first. You can still open any topic — this only helps you pick an order.

Prerequisites:Discrete Mathematics
Learning Path
07 stages
10 topics · 7 stages0/10 completed
01

Fundamentals

Start from zero. Build the vocabulary.

Probability Foundations~60 minbeginnerConditional Probability and Bayes' Theorem~75 minintermediate
02

Core Concepts

The ideas you cannot skip.

Random Variables and Distributions~90 minintermediateExpectation and Variance~75 minintermediate
03

Intermediate

Connect ideas and go deeper.

Common Distributions~90 minintermediateLaw of Large Numbers and CLT~60 minadvanced
04

Advanced

Beyond the basics.

Markov Chains~75 minadvanced
05

Practice

Solve problems until it sticks.

Practice Set: Probability Problems~180 minintermediate
06

Projects

Apply everything in real work.

Project: Monte Carlo Simulator~300 minintermediate
07

Interview Prep

Convert knowledge into offers.

Probability Interview Questions~90 minadvanced