With a good balance of theory and practice, the book effectively combines machine learning (ML) and artificial intelligence (AI) topics. Unlike other books on AI, Machine learning and artificial intelligence is not very mathematically intensive, which makes it easier to read. Overall, its language is very easy to follow. Each chapter has introduction and conclusion sections, and many helpful figures explain the concepts.

The book is divided into six parts: “Introduction,” “Machine Learning,” “Building End to End Pipelines,” “Artificial Intelligence,” “Implementations,” and “Conclusion.” Part 1 introduces the concepts of AI and ML, and their histories. The second and third parts explain ML algorithms such as decision trees and support vector machines (SVMs). Part 4 explains AI techniques such as classification and regression. The fifth part introduces cloud-based ML platforms such as Azure and Amazon. This part will be especially beneficial to readers who want hands-on applications in ML/AI. Helpful screen shots are included to explain the steps; however, they will need to be updated in future editions, as these tools change very rapidly.

A GitHub repository for the introduced code and coding exercises would have been helpful. This would make the book more useful, as AI/ML cannot be understood without coding.

Overall, the book is good for undergraduate and graduate students, researchers, scientists, and professionals.