Scikit-learn (http://scikit-learn.org/) has emerged as one of the most popular open source machine learning toolkits, now widely used in academia and industry. scikit-learn provides easy-to-use interfaces in Python to perform advanced analysis and build powerful predictive models.
This talk will cover basic concepts of machine learning, such as supervised and unsupervised learning, cross-validation and model selection, and how they map to programming concepts in scikit-learn. Andreas will demonstrate how to prepare data for machine learning, and go from applying a single algorithm to building a machine learning pipeline. We will cover the trade-offs of learning on large datasets, and describe some techinques to handle larger-than-RAM and streaming data on a single machine.
Andreas is a Research Engineer at the NYU Center for Data Science, building open source software for data science. Previously he worked as a Machine Learning Scientist at Amazon, focusing on computer vision and forecasting problems. He is one of the core developers of the scikit-learn machine learning library, and has co-maintained it for several years. 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.
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