Название: Applied Machine Learning and AI for Engineers: Solve Business Problems That Can't Be Solved Algorithmically (Final Release)
Автор: Jeff Prosise
Издательство: O’Reilly Media, Inc.
Год: 2023
Страниц: 428
Язык: английский
Формат: True/Retail PDF EPUB
Размер: 64.7 MB

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.

Analytics, AI, and Machine Learning do not have insurmountable technical or deployment issues. Rather, the challenge and accessibility of understanding just what is happening and how they work is the impediment, because this understanding is often shrouded in technical jargon and industry shibboleths. These barriers to entry act as a limiting principle and stunt the utilization of data science to address problems and questions. That is what this book seeks to change: it removes the shroud and jargon, making these tools and resources accessible.

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

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. It seeks to impart an intuitive understanding and resorts to equations only when necessary. Despite what you may have heard, you don’t have to be an expert in calculus or linear algebra to build systems that recognize objects in photos, translate English to French, or expose drug traffickers and tax cheats.