Andreas Müller walks you through a variety of real-world datasets using Jupyter notebooks together with the data analysis packages pandas, seaborn, Matplotlib, and scikit-learn. You’ll perform an initial assessment of data, deal with different data types, visualization, and preprocessing, and build predictive models for tasks such as health care and housing. The goal of this tutorial is to make you comfortable using Jupyter to do interactive data analysis and exploration—in particular making use of the very immediate feedback that Jupyter provides.
Andreas Müller is a lecturer at the Data Science Institute at Columbia University and author of Introduction to Machine Learning with Python (O’Reilly), which describes a practical approach to machine learning with Python and scikit-learn. His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science, and democratize the access to high-quality machine learning algorithms. Andreas is one of the core developers of the scikit-learn machine learning library and has been comaintaining it for several years. He is also a Software Carpentry instructor. Previously, he worked at the NYU Center for Data Science on open source and open science and as a machine learning scientist at Amazon.
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