A Knowledge Base on Machine Learning and Statistics.

Contents

• Introduction
  • Math Basics
  • Statistics basics
  • Machine Learning Basics
• Data preparation
  • Quality of a dataset
  • Data validation
  • Feature engineering
  • Feature selection
  • Train, validation, and test sets
  • Scaling
• Machine Learning models
  • Linear models
  • ML Models
  • k-Nearest Neighbors
  • Decision Tree
  • Gradient Boosting
  • Hyperparameters - Gradient Boosting
• Model evaluation
  • Model evaluation
  • Performance metrics
  • Train and cross validation
  • y-Randomization
  • Partial dependance plots
  • SHAP (SHapley Additive exPlanations)
• Scikit-Learn
  • Loading and saving data
  • Data preparation
  • Scale data
  • Train a model
  • Make predictions
  • Plot Experimental vs Calculated

Resources

• Scientific articles
• Reading material
• Python ML tools and packages