Checkpoint: Estimation and Inference
This lesson includes a 10-question quiz.
About this course
Part of Mathematics for Machine Learning 3: Probability and Statistics for Data-Driven Models — Gain the probability and statistics fluency models rely on: reason about distributions, apply Bayes and maximum likelihood, and quantify uncertainty in real data.
- Level
- Intermediate
- Goal
- Fundamentals
- Language
- English
- Focus
- probability distributions Bayes' rule maximum likelihood statistical inference sampling and uncertainty
- Prior knowledge
- Basic calculus (derivatives, integrals), Vectors and matrices, Some Python for small simulations
- Practice
- In-depth examples
- Time
- ~5.3h
- Voice
- a data scientist who grounds every distribution in a real dataset question before showing the formula
- Not covered
- Measure-theoretic probability, Bayesian deep learning, Causal inference
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