Getting Started with Machine Learning in Python /

A+ guide to using Machine Learning to classify objects, predict future prices, and automatically learn fixes to problems About This Video Learn about supervised learning: how to classify data points and predict future numbers Practical exercises on unsupervised learning: how to segment clients and c...

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Bibliographic Details
Main Author: Lai, Rudy (Author)
Corporate Author: Safari, an O'Reilly Media Company
Format: eBook
Language:English
Published: Packt Publishing, 2018.
Edition:1st edition.
Subjects:
Online Access:Connect to this electronic resource
Description
Summary:A+ guide to using Machine Learning to classify objects, predict future prices, and automatically learn fixes to problems About This Video Learn about supervised learning: how to classify data points and predict future numbers Practical exercises on unsupervised learning: how to segment clients and cluster documents Intuition-driven practical tour through Machine Learning, packed with step-by-step instructions, working examples, and helpful advice In Detail Machine Learning is a hot topic. And you want to get involved! From developers to analysts, this course aims to bring Machine Learning to those with coding experience and numerical skills. In this course, we introduce, via intuition rather than theory, the core of what makes Machine Learning work. Learn how to use labeled datasets to classify objects or predict future values, so that you can provide more accurate and valuable analysis. Use unlabelled datasets to do segmentation and clustering, so that you can separate a large dataset into sensible groups. If you want to move past Excel and if-then-else into automatically learned ML solutions, this course is for you! All the code and the supporting files are available on GitHub at - https://github.com/PacktPublishing/Getting-Started-with-Machine-Learning-in-Python-
Item Description:Videorecording.
Physical Description:1 online resource (1 video file, approximately 2 hr., 53 min.)
Format:Mode of access: World Wide Web.