Summary ML Notes

Part 1: Preprocessing the Data
* Missing Data
* Categorical Data
	* Dummy variables
* Training and Testing split
* Feature scaling
	* Някой алгоритми не се нуждаят от това, но други се нуждаят
	* Препоръчително е да се прави

Regression
* Singe Linear Regression
* Multiple Linear Regression
* Polynomial Regression
	* parabolic effect
	* Example: used to describe how diseases spread or pandemics and epidemics spread across territory, across population
	* It's a special case of multiple linear regression
* Support Vector Regression (SVR)
* Decision Tree Regression
* Random Forest Regression

* Evaluating Regression Models Performance
	* R-Squared
	* Adjusted R-Squared
	* Linear Regression Coefficients

Classification
* Logistic Regression
* K-Nearest Neighbours (K-NN)
* Support Vector Machine (SVM)
* Kernel SVM
* Naive Bayes
* Decision Tree Classifier
* Random Forest Classification

* Evaluating Classification Models Performance
	* False Positive and False Negatives
	* Confusion Matrix
	* Accuracy Paradox
	* CAP Curve
	* CAP Curve Analysis

Clustering
* K-means Clustering
* Hierarchical Clustering
	* Dendograms

Association Rule Learning
* Apriori
* Eclat

Reinforcement Learning
* Upper Confidence Bound (UCB)
* Thompson Sampling