Probability & Statistics

Modeling Uncertainty

Machine Learning is not magic; it is Probability with a fancy name. In this module, we move from the deterministic world of “If-Else” to the probabilistic world of “Maybe”. —

Probability & Statistics

[!NOTE] This module explores the core principles of Probability & Statistics, deriving solutions from first principles and hardware constraints to build world-class, production-ready expertise.

1. The Roadmap: Module 03

🎲
Axioms
Bayes' Theorem
📊
Distributions
Gaussian & Poisson
📏
Expectation
Variance & Covariance
🧪
Testing
Hypothesis & P-Values
🤖
Application
Naive Bayes Filter

2. Chapters

  1. Probability Axioms - The language of uncertainty.
  2. Distributions - The shape of data (Gaussian, Poisson).
  3. Expectation & Variance - Summarizing chaos.
  4. Hypothesis Testing - Signal vs Noise.
  5. Case Study: Naive Bayes - Building a Spam Filter.
  6. Module Review - Flashcards and Cheat Sheet.

Module Chapters