Source code for the "Learning scikit-learn: Machine Learning in Python"
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Chapter 1 - A Gentle Introduction to Machine Learning.ipynb
Chapter 1 (2nd ed.) - A Gentle Introduction to Machine Learning with Python and Scikit-learn.ipynb
Chapter 2 - Supervised Learning - Image Recognition with Support Vector Machines.ipynb
Chapter 2 - Supervised Learning - Regression.ipynb
Chapter 2 - Supervised Learning - Text Classification with Naive Bayes.ipynb
Chapter 2 - Supervised learning - Explaining Titanic Hypothesis with Decision Trees.ipynb
Chapter 3 - Unsupervised Learning - Clustering Handwritten Digits.ipynb
Chapter 3 - Unsupervised Learning - Principal Component Analysis.ipynb
Chapter 4 - Advanced Features - Feature Engineering and Selection.ipynb
Chapter 4 - Advanced Features - Model Selection.ipynb
Library versions, readme.ipynb
README.md
titanic.png