I Feature Engineering
1.3 Feature Scaling (Standardization & Normalization)
1.5 Outlier Detection
1.6 Feature Extraction : Principal Component Analysis
II Supervised Learning : Regression
2.2 Multiple Linear Regression
2.3 Regression Model Weight Optimization using Gradient Descent
2.4 Non Linear Model - Polynomial Regression
III Supervised : Classification
3.1 Binomial Classification : Logistic Regression
3.3 K Nearest Neighbor (KNN)
3.5 Decision Tree
IV Time Series Analysis
4.1 Data Conversion
4.3 Auto Regression
4.4 Bitcoin Time series analysis using Fbprophet