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

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

這本書名為「應用於工程師的機器學習和人工智慧」，作者Jeff Prosise幫助工程師和軟體開發人員建立對人工智慧的直觀理解，以解決商業問題。本書避免使用數學，而是提供實用的指導，讓讀者學會將人工智慧和機器學習應用於公司業務。

本書提供了Jeff Prosise在全球各公司和研究機構教授的人工智慧和機器學習課程的實例和插圖。書中沒有冗長的內容，也沒有可怕的方程式，只有為工程師和軟體開發人員提供的快速入門指南，並附有實際的範例。

本書幫助讀者：
- 了解機器學習和深度學習的定義和能力
- 理解常見的學習演算法以及何時應用它們
- 使用Scikit-Learn在Python中建立機器學習模型，使用Keras和TensorFlow建立神經網路
- 訓練和評估回歸模型、二元和多類別分類模型
- 建立人臉識別模型和物體檢測模型
- 建立能回應自然語言查詢並將文字翻譯成其他語言的語言模型
- 使用認知服務將人工智慧應用於您所開發的應用程式中。