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Machine Learning Scientist with Python
Introduction
Supervised Learning with scikit-learn
Classification
Regression
Fine-Tuning Your Model
Preprocessing and Pipelines
Unsupervised Learning in Python
Clustering for dataset exploration
Visualization with hierarchical clustering and t-SNE
Decorrelating your data and dimension reduction
Discovering interpretable features
Linear Classifiers in Python
Applying logistic regression and SVM
Loss functions
Logistic regression
Support Vector Machines
Machine Learning with Tree-Based Models in Python
Classification and Regression
The Bias-Variance Tradeoff
Bagging and Random Forests
Boosting
Model Tuning
Extreme Gradient Boosting with XGBoost
Classification with XGBoost
Regression with XGBoost
Fine-tuning your XGBoost model
Using XGBoost in pipelines
repository
open issue
Index
