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{ "category": "SciPy 2012", "language": "English", "slug": "scikit-learn", "speakers": [ "Jake Vanderplas" ], "tags": [ "advanced" ], "id": 1342, "state": 1, "title": "scikit-learn", "summary": "", "description": "Machine Learning has been getting a lot of buzz lately, and many software\nlibraries have been created which implement these routines. scikit-learn is a\npython package built on numpy and scipy which implements a wide variety of\nmachine learning algorithms, useful for everything from facial recognition to\noptical character recognition to automated classification of astronomical\nimages. This tutorial will begin with a crash course in machine learning and\nintroduce participants to several of the most common learning techniques for\nclassification, regression, and visualization. Building on this background, we\nwill explore several applications of these techniques to scientific data -- in\nparticular, galaxy, star, and quasar data from the Sloan Digital Sky Survey --\nand learn some basic astrophysics along the way. From these examples, tutorial\nparticipants will gain knowledge and experience needed to successfully solve a\nvariety of machine learning and statistical data mining problems with python.\n\n", "quality_notes": "", "copyright_text": "CC BY-SA", "embed": "<object width=\"640\" height=\"390\"><param name=\"movie\" value=\";hl=en_US\"></param><param name=\"allowFullScreen\" value=\"true\"></param><param name=\"allowscriptaccess\" value=\"always\"></param><embed src=\";hl=en_US\" type=\"application/x-shockwave-flash\" width=\"640\" height=\"390\" allowscriptaccess=\"always\" allowfullscreen=\"true\"></embed></object>", "thumbnail_url": "", "duration": null, "video_ogv_length": null, "video_ogv_url": null, "video_ogv_download_only": false, "video_mp4_length": null, "video_mp4_url": "", "video_mp4_download_only": false, "video_webm_length": null, "video_webm_url": "", "video_webm_download_only": false, "video_flv_length": null, "video_flv_url": "", "video_flv_download_only": false, "source_url": "", "whiteboard": "", "recorded": "2012-07-16", "added": "2012-09-13T13:40:18", "updated": "2014-04-08T20:28:27.178" }