statistical_methods_ML

This repository focuses on practical statistical techniques for ML using theory, abstract topics and APIs.

To-Do Topics

1. Introduction to Statistics -> ✅
2. Statistics vs Machine Learning -> ✅
3. Examples of Statistics in Machine Learning -> ✅

4. Gaussian and Summary Stats -> ✅
5. Simple Data Visualization -> ✅
6. Random Numbers -> ✅
7. Law of Large Numbers -> ✅
8. Central Limit Theorem -> ✅

9. Statistical Hypothesis Testing -> ✅
10. Statistical Distributions -> ✅
11. Critical Values -> ✅
12. Covariance and Correlation -> ✅
13. Significance Tests -> ✅
14. Effect Size -> ✅
15. Statistical Power -> ✅

16. Introduction to Resampling
17. Estimation with Bootstrap
18. Estimation with Cross-Validation
19. Introduction to Estimation Statistics
20. Tolerance Intervals
21. Confidence Intervals
22. Prediction Intervals
23. Rank Data
24. Normality Tests
25. Make Data Normal
26. 5-Number Summary
27. Rank Correlation
28. Rank Significance Tests
29. Independence Test