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

Description

Applied Machine Learning and AI for Engineers: Solve Business Problems That Can’t Be Solved Algorithmically is a practical guide for engineers who want to apply machine learning and artificial intelligence to real-world business challenges.

Rather than focusing on abstract theory, this book emphasizes applied problem-solving—showing how ML and AI can be used when deterministic, rule-based algorithms are no longer effective. Readers learn how to identify suitable problems for machine learning, frame them correctly, select appropriate models, and evaluate results in production environments.

The book covers essential topics such as supervised and unsupervised learning, feature engineering, model evaluation, and deployment strategies, all explained from an engineering perspective. Through real-world examples and business-driven use cases, engineers gain the skills needed to design intelligent systems that deliver measurable impact.

Ideal for software engineers, data engineers, and technical professionals, this book bridges the gap between traditional engineering approaches and modern AI-driven solutions. It equips readers with the mindset and tools required to build scalable, data-driven systems that solve complex business problems.

Key benefits include:
Practical machine learning and AI for engineers
Real-world business problem-solving with ML
Techniques beyond rule-based and algorithmic solutions
Production-focused ML workflows and evaluation