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Contents
Introduction
Math Basics
Statistics basics
Boxplots
Outliers
Parametric tests or models
Machine Learning Basics
Types of ML systems
Machine Learning workflow
Data preparation
Quality of a dataset
Data validation
Feature engineering
RDKit
MACCS fingerprints
Morgan ECFP fingerprints
Mordred
Measure molecular similarity
Feature selection
Train, validation, and test sets
Scaling
Machine Learning models
Linear models
Linear regression
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
Pipelines
Saving Scikit-learn model for reuse
Resources
Scientific articles
Reading material
Python ML tools and packages
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