Название: Applied Machine Learning and AI for Engineers: Solve Business Problems That Can’t Be Solved Algorithmically (Second Early Release)Автор: Jeff Prosise
Издательство: O’Reilly Media, Inc.
Год: 2022-09-12
Страниц: 207
Язык: английский
Формат: epub (true), mobi
Размер: 16.3 MB
Many introductory guides to AI are mathematics 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 defective parts coming off an assembly line? This practical book teaches you the skills necessary to put AI and Machine Learning (ML) to work at your company.
This book contains numerous code samples that you can run to reinforce the concepts presented in each chapter. Most are written in Python and use popular open-source libraries such as Scikit-learn, Keras, and TensorFlow. All are available in a public GitHub repo. It’s the single source of truth for the code samples because I can update it at any time.
Who Should Read This Book:
Recently, I received a call from the head of engineering at a manufacturing company. He started the conversation like this: “Until last week, I didn’t know what ML and AI stood for. Now my CEO has tasked me with figuring out how they can improve our business, and to do it before our competitors get ahead of us. I am starting at square one. Can you help?”
The next call came from a government-contracting firm interested in using machine learning to detect tax fraud and money laundering. The team there was reasonably well versed in machine-learning theory but wondered how best to go about building the models they needed.
Professionals everywhere are realizing that ML and AI represent a technological tsunami, and they’re trying to get on top of the wave before it crashes over them. This book is for them: engineers, software developers, IT managers, and others whose goal is to build a practical understanding of ML and AI and put that knowledge to work solving problems that were difficult or even intractable before.
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