Probability

#Probability & Statistics Before diving into Machine Learning proper, here's some notes about Probability Theory and standard Statistical Methods. If you really want to understand and grasp what any Machine Learning algorithm or model actually does, this is is the place to start,

These notes were put together from a variety of lecture notes from my university courses as well as from Probability and Statistics for Engineering and the Sciences by Jay L. Devore

1. [Fundamentals and Distribution Reference](1 - Fundamentals and Distributions Reference.pdf)
2. [Random Variable Properties and Central Limit Theorem](2 - RV Properties and CLT.pdf)
3. [Estimation](3 - Estimation.pdf)
4. [Hypothesis Testing](4 - Hypothesis Testing.pdf)
5. [ANOVA](5 - ANOVA.pdf)
6. [NonParametric Tests](6 - NonParametric Tests.pdf)
7. [Stochastic Processes](7 - Stochastic Processes.pdf)
8. [Markov Chains](8 - Markov Chains.pdf)
9. [Hypothesis Testing Recap Diagram](9 - Tests Diagram.pdf)