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Published on Dec 9, 2014
PyData NYC 2014 Scikit-Learn is one of the most popular machine learning library written in Python, it has quite active community and extensive coverage for a number of machine learning algorithms. It has feature extraction, feature and model selection algorithms, and validation methods as well to build a modern machine learning pipeline. This tutorial introduces common recipes to build a modern machine learning pipeline for different input domains and show how one might construct the components using advanced features of Scikit-learn. Specifically, I will introduce feature extraction methods using image and text, and show how one may use feature selection methods to reduce the input dimension space and remove the features which are not useful for classification. For optimization, I will show model selection methods using parameter search. Last in the pipeline, I will show validation methods to be able to choose best parameters. After building the pipeline, I will also show how one might deploy the model into production.