IntermediateContent in Spanish
Statistics and probability for machine learning
Build the mathematical foundations every data scientist needs before training models. You will master probability, random variables and the key distributions (normal, binomial and Poisson), Bayes' theorem, inference and maximum likelihood estimation. You will finish with the notions of linear algebra and calculus (vectors, matrices, gradient) and the bias-variance trade-off that underpin machine learning. It is the second course of the Professional Certificate in Data Science.
9 lessons 10 hours 700 XP
Start the courseSyllabus
Module 1 · Probability fundamentals
- 1Probability: the language of uncertainty45 min
- 2Random variables and expected value50 min
Module 2 · Distributions and Bayes
- 3Discrete distributions: binomial and Poisson50 min
- 4The normal distribution and the Central Limit Theorem50 min
- 5Conditional probability and Bayes' theorem50 min
Module 3 · Inference and estimation
- 6Statistical inference: intervals and tests50 min
- 7Maximum likelihood estimation (MLE)50 min
Module 4 · Mathematics for ML
- 8Correlation, causation and the bias-variance trade-off45 min
- 9Essential linear algebra and calculus for ML50 min