Applied Machine Learning and AI for Engineers

Applied Machine Learning and AI for Engineers

Jeff Prosise takes a practical, engineer-friendly approach to machine learning and AI in this focused guide from O’Reilly. Rather than leaning on dense mathematics, the book stays centered on the real question many developers face: how do these tools help solve business problems that are not easily handled with traditional algorithms?

Built for hands-on learning 💻

The material moves through core machine learning ideas, common learning algorithms, and the basic workflow needed to build and evaluate models. Readers can expect coverage of regression, binary and multiclass classification, and the kind of conceptual grounding that makes it easier to choose the right approach for a given problem.

From classical ML to deep learning

The book also expands into deep learning and practical AI development with Python, Scikit-Learn, Keras, and TensorFlow. Topics include facial recognition, object detection, language models, natural-language query handling, and translation, along with use of Cognitive Services to add AI capabilities to applications.

Why this book stands out

This is a technical book, but it is written for readers who want clarity before theory-heavy detail. The result is a useful bridge between first exposure to machine learning and the point where you can begin applying it with more confidence in real projects.

Who will appreciate it

• Engineers and software developers exploring applied AI
• Readers who prefer examples and implementation over abstract theory
• Developers looking for an approachable path into machine learning, deep learning, and AI services

If you want a grounded introduction to machine learning that keeps one eye on real-world use cases, this ebook offers a strong, practical starting point.