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Cloud Analytics with Google Cloud Platform

You're reading from   Cloud Analytics with Google Cloud Platform An end-to-end guide to processing and analyzing big data using Google Cloud Platform

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788839686
Length 282 pages
Edition 1st Edition
Concepts
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Author (1):
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Sanket Thodge Sanket Thodge
Author Profile Icon Sanket Thodge
Sanket Thodge
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Table of Contents (11) Chapters Close

Preface 1. Introducing Cloud Analytics FREE CHAPTER 2. Design and Business Considerations 3. GCP 10,000 Feet Above – A High-Level Understanding of GCP 4. Ingestion and Storing – Bring the Data and Capture It 5. Processing and Visualizing – Close Encounter 6. Machine Learning, Deep Learning, and AI on GCP 7. Guidance on Google Cloud Platform Certification 8. Business Use Cases 9. Introduction to AWS and Azure 10. Other Books You May Enjoy

Machine learning


Machine learning (ML) is a part of an artificial intelligence developed for the technological development of human knowledge. ML provisions devices to dynamically handle any situation through analysis, self-training, observation, and experience, which makes continuous improvement of decisions in subsequent scenarios. ML is ambiguous with data mining in databases for knowledge engineering. It is focused on predictions based on known facts learned from training data, while data mining focuses on the discovery of unknown facts. Data mining uses many ML methods, whereas ML uses unsupervised learning to improve the user’s accuracy. Machine learning and statistics are very closely tied domains.

In computational learning theory, a computation is considered feasible if it can be done in polynomial time. Some classes of functions can be learned in polynomial time and others cannot. The approaches of ML are decision tree learning, association rule learning, artificial neural networks...

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