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

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.

Why I Wrote This Book

Inside every author is a tiny gremlin that says they can tell the story in a way that no one else has. I wrote my first computer book more than 30 years ago and my last one more than 20 years ago, and I didn’t intend to write another one. But now I have a story to tell. It’s an important story—one that every engineer and software developer should hear. I’m not entirely satisfied with the way others have told it, so I wrote the book that I wish I had had when I was learning the craft. It starts with the basics and leads you on a journey to the heights of ML and AI. By the end, you’ll understand how credit card companies detect fraud, how aircraft companies use machine learning to perform predictive maintenance on jet engines, how self-driving cars see the world around them, how Google Translate translates text between languages, and how facial recognition systems work. Moreover, you’ll be able to build systems like them yourself, or use existing systems to infuse AI into the apps that you write.

Today’s most advanced machine learning models are trained on computers equipped with graphics processing units (GPUs) or tensor processing units (TPUs), often at great time and expense. A point of emphasis in this book is presenting examples that can be built on a typical PC or laptop without a GPU. When we tackle computer-vision models that recognize objects in photos, I’ll describe how such models work and how they’re trained with millions of images on GPU clusters. But then I’ll show you how to use a technique called transfer learning to repurpose existing models to solve domain-specific problems and train them on an ordinary laptop.

This book draws heavily from the classes and workshops that I teach at companies and research institutions around the world. I love teaching because I love seeing the light bulbs come on. I often kick off classes on ML and AI by saying I’m not here to teach; I’m here to change your life. Here’s hoping that your life will be a little bit different, and a little bit better, than it was before you read this book.

About the Author

Jeff Prosise is an engineer whose passion is to introduce other engineers and software developers to the wonders of AI and machine learning. Cofounder of Wintellect, he has written nine books and hundreds of magazine articles, trained thousands of developers at Microsoft, and spoken at some of the world's largest software conferences. In another life, Jeff worked on high-powered laser systems and fusion-energy research at Oak Ridge National Laboratory and Lawrence Livermore National Laboratory. In his spare time, he builds and flies large radio-control jets and goes out of his way to get wet in the world's best dive spots. Following the acquisition of his company in 2021, Jeff serves as chief learning officer at Atmosera, where he helps customers infuse AI into their products.