Prerequisites

• Linear Algebra
• Matrix Calculus
• Probability & Statistics

Topics

Basics

• Bias & Variance
• Regularization & Norms
• Optimization

Regression

1. Simple/Multiple Linear Regression
2. Polynomial Regression
3. Logistic Regression
4. Support Vector Regression
5. Decision Treee Regression
6. Random Forest Regression

Classification

1. Logistic Regression
2. K-Nearest Neighbors
3. Support Vector Machine (SVM)
4. Kernel SVM
5. Naive Bayes
6. Decision Tree Classification
7. Random Forest Classification

Clustering

1. K-Means Clustering
2. Hierarchical Clustering

Association Rule Learning

1. Apriori
2. Eclat

Dimensionality Reduction

1. Independent Component Analysis (ICA)
2. Principal Component Analysis (PCA)
3. Kernel PCA
4. Linear Discriminant Analysis (LDA)

Boosting - Combining many classifiers

1. XGBoost
2. Adaboost

& so on..