Introduction# Basic concepts and definitions on math, statistics, and machine learning. Math Basics Monotone function Dense vs sparse matrices Statistics basics Dividing data Percentiles (100 regions) Deciles (10 regions) Quartiles (4 regions) Hinges Example 1: sample size of 20 Example 2: sample size of 21 Five Number Summary Density plot References Contents Boxplots Definitions Outliers Creating a boxplot in Pandas References Outliers Definition of outliers Ways to describe data Box plot construction Box plots with fences Outlier detection criteria Extreme Outliers Mild Outliers Parametric tests or models Parametric models Non-parametric models Choosing Between Parametric and Non-Parametric Models Machine Learning Basics Features Target Hyperparameters Bias-Variance Tradeoff Overfitting and underfitting Contents Types of ML systems Supervised/unsupervised Supervised learning Unsupervised learning Semisupervised learning Reinforcement learning Machine Learning workflow 1) Data preparation 2) Feature engineering 3) Model development 4) Model testing 5) Application References
