Probability & Basics of Statistics

Probability & Statistics for ML — Build the Intuition, Not Just the Formula

Every ML algorithm has statistics underneath it. When your model is uncertain, that's probability. When it's learning from error, that's statistics. When it's deciding what's signal and what's noise, that's both. You can use ML tools without understanding any of this — until something breaks and you have no idea why.

This resource is built for people who want to understand what's actually happening inside the models they build, not just how to run them.

What's covered:

1. Probability fundamentals — events, distributions, conditional probability, Bayes theorem
2. Descriptive statistics — mean, median, variance, standard deviation and when each one lies to you
3. Distributions that matter in ML — Normal, Binomial, Poisson, and why your data's shape changes everything
4. Hypothesis testing — p-values, confidence intervals, and how to actually interpret them
5. Correlation vs causation — the mistake that breaks more analyses than any coding error
6. How these concepts show up directly in algorithms — Naive Bayes, linear regression, gradient descent, and more
7. A/B testing fundamentals — designing experiments and reading results without fooling yourself

Who this is for:

Anyone in data, analytics, or ML who nods along when statistics come up but privately isn't confident they truly understand what the numbers mean — and wants to fix that permanently, not just for an interview.

The math isn't the hard part. The intuition is. This is where you build it.