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Applied Machine Learning and AI for Engineers: Solve Business Problems That Can't Be Solved Algorithmically

Applied Machine Learning and AI for Engineers: Solve Business Problems That Can't Be Solved Algorithmically

Applied Machine Learning and AI for Engineers: Solve Business Problems That
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Applied Machine Learning and AI for Engineers: Solve Business Problems That Can't Be Solved Algorithmically Papeback -

by Jeff Prosise

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  • Title Applied Machine Learning and AI for Engineers: Solve Business Problems That Can't Be Solved Algorithmically
  • Author Jeff Prosise
  • Binding Papeback
  • Condition New
  • Pages 425
  • Volumes 1
  • Language ENG
  • Publisher O'Reilly Media
  • Publication date
  • Illustrated Yes
  • Features Illustrated, Index
  • Bookseller's Inventory # 6395194722
  • ISBN 9781492098058 / 1492098051
  • Weight 1.49 lbs (0.68 kg)
  • Dimensions 9.19 x 7 x 0.87 in (23.34 x 17.78 x 2.21 cm)
  • Category Computers - General Information
  • Library of Congress subjects Machine learning
  • Dewey Decimal Code 006.31
  • Quantity available 3

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From the publisher

While many introductory guides to AI are calculus books in disguise, this one mostly eschews the math. Instead, author Jeff Prosise helps engineers and software developers build an intuitive understanding of AI to solve business problems. Need to create a system to detect the sounds of illegal logging in the rainforest, analyze text for sentiment, or predict early failures in rotating machinery? This practical book teaches you the skills necessary to put AI and machine learning to work at your company.

Applied Machine Learning and AI for Engineers provides examples and illustrations from the AI and ML course Prosise teaches at companies and research institutions worldwide. There's no fluff and no scary equations--just a fast start for engineers and software developers, complete with hands-on examples.

This book helps you:

  • Learn what machine learning and deep learning are and what they can accomplish
  • Understand how popular learning algorithms work and when to apply them
  • Build machine learning models in Python with Scikit-Learn, and neural networks with Keras and TensorFlow
  • Train and score regression models and binary and multiclass classification models
  • Build facial recognition models and object detection models
  • Build language models that respond to natural-language queries and translate text to other languages
  • Use Cognitive Services to infuse AI into the apps that you write

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