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210123 ||| eng |
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|a Esposito, Dino
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| 245 |
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|a Introducing Machine Learning
|c Esposito, Dino
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| 250 |
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|a 1st edition
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| 260 |
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|b Microsoft Press
|c 2020
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| 300 |
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|a 400 pages
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| 653 |
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|a Machine learning / http://id.loc.gov/authorities/subjects/sh85079324
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| 653 |
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|a Machine learning / fast
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| 653 |
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|a Apprentissage automatique
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| 700 |
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|a Esposito, Francesco
|e author
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 500 |
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|a Made available through: Safari, an O'Reilly Media Company
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| 776 |
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|z 9780135588338
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| 776 |
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|z 9780135565667
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| 856 |
4 |
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|u https://learning.oreilly.com/library/view/~/9780135588338/?ar
|x Verlag
|3 Volltext
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| 082 |
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|a 006.3/1
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| 520 |
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|a Master machine learning concepts and develop real-world solutions Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and the basic steps of any machine learning project. Next, they introduce Microsoft's powerful ML.NET library, including capabilities for data processing, training, and evaluation. They present families of algorithms that can be trained to solve real-life problems, as well as deep learning techniques utilizing neural networks. The authors conclude by introducing valuable runtime services available through the Azure cloud platform and consider the long-term business vision for machine learning. · 14-time Microsoft MVP Dino Esposito and Francesco Esposito help you · Explore what's known about how humans learn and how intelligent software is built · Discover which problems machine learning can address · Understand the machine learning pipeline: the steps leading to a deliverable model · Use AutoML to automatically select the best pipeline for any problem and dataset · Master ML.NET, implement its pipeline, and apply its tasks and algorithms · Explore the mathematical foundations of machine learning · Make predictions, improve decision-making, and apply probabilistic methods · Group data via classification and clustering · Learn the fundamentals of deep learning, including neural network design · Leverage AI cloud services to build better real-world solutions faster About This Book · For professionals who want to build machine learning applications: both developers who need data science skills and data scientists who need relevant programming skills · Includes examples of machine learning coding scenarios built using the ML.NET library
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