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The new edition of this popular professional book on Artificial Intelligence (ML) and Machine Learning (ML) has been revised for classroom or training use. The new edition provides comprehensive coverage of combined AI and ML theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and their origin and current state. The second and third parts delve into conceptual and theoretic aspects of static and dynamic ML techniques. The fourth part describes the practical applications where presented techniques can be applied. The fifth part introduces the user to some of the implementation strategies for solving real life ML problems. Each chapter is accompanied with a set of exercises that will help the reader / student to apply the learnings from the chapter to a real-life problem. Completion of these exercises will help the reader / student to solidify the concepts learned.
The second part of the book deals with the theoretical foundation of Machine Learning. In this part we will study various algorithms, as well as their origins and their applications. Machine Learning has united a vast array of algorithms that find their origins in fields ranging from electrical engineering, signal processing, statistics, financial analysis, genetic sciences, and so on. All these algorithms are primarily developed from the principles of pure mathematics and statistics, in spite of being originated in fundamentally different areas. Along with the roots, they also have one more thing in common: use of computers to automate complex computations. These computations ultimately lead to solving problems that seem so hard that one would believe that they are solved by some intelligent entity or artificial intelligence.
We are also going to learn how to implement the concepts learnt in each of the chapter using open-source Python libraries. Python has become de facto standard for implementing the machine learning algorithms due to support from the community as well as from commercial platforms from companies like Amazon, Google, and Microsoft. Also most of the cutting-edge research happening in the field is readily accessible in the form of open-source libraries in Python. We will use Google’s AI platform to implement the code, due to its simple setup and easy access to all the libraries. However, I will outline the setup process for Microsoft’s platform and use that for some implementations as well.
The third part is dedicated to building end-to-end pipelines for solving real-life problems using the techniques learned in the previous section. Real-life problems always pose unique challenges that need to be addressed in custom manner. However, there are certain broader steps one can take to tackle most of the problems, and this section focusses on these aspects of ML and AI. This section really ties together theory and practice to give readers a glimpse into the real world.
The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals. It makes minimal use of mathematics to make the topics more intuitive and accessible. The book covers a large gamut of topics in the area of AI and ML and a professor can tailor a course on AI / ML based on the book by selecting and re-organizing the sequence of chapters to suit the needs