De-Mystifying Math and Stats for Machine Learning

Table of Contents

1. Introduction
2. Central Tendency vs. Dispersion
3. Types of Distribution
4. Precision vs. Accuracy
5. Percentiles
6. Dependent Vs. Independent Variables
7. Types of Data
8. Population Vs. Sample
9. Hypothesis Testing
10. Outliers
11. Machine Learning Concepts
12. Measuring Accuracy in Algorithms
13. Maths behind Regression
14. Linear Algebra in Machine Learning
15. Decision Tree
16. k Nearest Neighbours (kNN)
17. Gradient Descent