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This tutorial session is an hands-on workshop on applied Machine Learning with the scikit-learn library. We will dive deeper into scikit-learn model evaluation and automated parameter tuning. We will also study how to scale text classification models for sentiment analysis or spam detection and use IPython.parallel to leverage multi-CPU or ad-hoc cloud clusters.
This tutorial will offer an introduction to the core concepts of machine learning, and how they can be easily applied in Python using Scikit-learn. We will use the scikit-learn API to introduce and explore the basic categories of machine learning problems, related topics such as feature selection and model validation, and the application of these tools to real-world data sets.
This tutorial will offer an in-depth experience of methods and tools for the Machine Learning practitioner through a selection of advanced features of scikit-learn and related projects. This tutorial targets developers already familiar with machine learning concepts and scikit-learn who wish to learn how to apply those tools on larger datasets using multicore machines or distributed clusters.
The goal of this tutorial is to give the attendee a first experience of machine learning tools applied to practical software engineering tasks such as language detection of tweets, topic classification of web pages, sentiment analysis of customer products reviews and facial recognition in pictures from the web or from your own webcam.