Introducing Machine Learning (Microsoft Press)
Wednesday, 11 March 2020

This book delivers practical knowledge to make the most of the opportunities offered by machine learning. Authors 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 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 and conclude by introducing runtime services available through the Azure cloud platform and consider the long-term business vision for machine learning.

<ASIN:0135565669>

 

Author: Dino Esposito and Francesco Esposito
Publisher: Microsoft Press
Date: March 2020
Pages: 400
ISBN: 978-0135565667
Print: 0135565669
Kindle: B084B189CB
Audience: developers interested in machine learning
Level: Intermediate/Advanced
Category: Artificial Intelligence

  • 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
  • Use AI cloud services to build better real-world solutions faster

 

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Author: Andrew Glassner
Publisher: No Starch Press
Date: July 2021
Pages: 750
ISBN: 978-1718500723
Print: 1718500726
Kindle: ‎ B085BVWXNS
Audience: Developers interested in deep learning
Rating: Mike James
Reviewer: 5
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Author: Dr. Sarah Kaiser and Dr. Chris Granade
Publisher: Manning
Date: June 2021
Pages: 384
ISBN: 978-1617296130
Print: 1617296139
Kindle: B098BNK1T9
Audience: Developers interested in quantum computing
Rating: 4.5
Reviewer: Mike James
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