PLURALSIGHT.PYTHON.UNDERSTANDING.MACHINE.LEARNING-THEOS

Section
Appz
Group
THEOS
Size
114,26 MB
Files
10
Date
2016-05-22

NFO

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███                                                                         ███
███                           T H E O S O P H I A                           ███
███                            .wisdom is here.                             ███
███                                                                         ███
███                             Proudly Presents                            ███
███                Understanding Machine Learning with Python               ███
███                                                                         ███
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███                                                                         ███
███  Release notes:                                                         ███
███  ──────────────                                                         ███
███                                                                         ███
███  Date.........: May 19, 2016                                            ███
███  Size.........: 08x15mb                                                 ███
███  Url..........: http://bit.ly/1Tje0hu                                   ███
███  Type.........: Bookware                                                ███
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███                                                                         ███
███  Description:                                                           ███
███  ────────────                                                           ███
███                                                                         ███
███  When working with data, machine learning can be used to do             ███
███  incredible things, including predicting future events. Its ease of     ███
███  use combined with the power of scikit-learn is causing Python to       ███
███  become the preferred development language for many machine learning    ███
███  practitioners. In this course, Understanding Machine Learning with     ███
███  Python, you will learn how Python developers and data scientists use   ███
███  machine learning to predict the likelihood of events based on data.    ███
███  Throughout this course, you will use Python and the scikit-learn       ███
███  library. Specifically, you will learn how to format your problem to    ███
███  be solvable, how to prepare your data for use in a prediction, and     ███
███  finally how to combine that data with algorithms to create models      ███
███  that can predict the future, all performed in the Jupyter Notebook     ███
███  environment. By the end of this course, you will have a better         ███
███  understanding of how machine learning can help you put your data to    ███
███  good use in predicting future events, and you'll also know how to      ███
███  use Pyt                                                                ███
███                                                                         ███
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███                                                                         ███
███  Install notes:                                                         ███
███  ──────────────                                                         ███
███                                                                         ███
███  Unpack, burn/mount and go learning                                     ███
███                                                                         ███
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███                                                                         ███
███  Greetings:                                                             ███
███  ──────────                                                             ███
███                                                                         ███
███  To all the groups who brings quality - you know who you are            ███
███                                                                         ███
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Files

PathSize
t-plpuml.r0014,31 MB
t-plpuml.r0114,31 MB
t-plpuml.r0214,31 MB
t-plpuml.r0314,31 MB
t-plpuml.r0414,31 MB
t-plpuml.r0514,31 MB
t-plpuml.r0614,12 MB
t-plpuml.rar14,31 MB
t-plpuml.sfv184 B
theos.nfo5,24 KB