Continuously educating yourself is important in any industry to stay up to date – but especially when it comes to complex topics like Artificial Intelligence, which are evolving rapidly. For this purpose, we present the seven most popular Artificial Intelligence books among our Data Scientists. Some of the books are also interesting for non-specialists who want to better understand basic concepts of AI and its various applications.
“Introduction to Machine Learning with Python” by Sarah Guido and Andreas C. Müller
As the book title suggests, the book focuses on providing practical knowledge on how to program your own machine learning applications using Python and the scikit-learn library. After all, the possible applications of ML are almost unlimited and not only interesting for large companies with dedicated research teams.
The book is written in a very clear and understandable way and doesn’t deal with the mathematical principles behind the applications. Therefore, it is ideal for beginners. Prior knowledge with the NumPy and matplotlib libraries is not mandatory, but will help in understanding the content.
In addition to basic machine learning concepts and applications, the book also offers suggestions at the end on how to further develop your skills in ML and Data Science.
“Pattern Recognition and Machine Learning” by Christopher M. Bishop
“Pattern Recognition and Machine Learning” provides a comprehensive introduction to the fields of pattern recognition and machine learning. The book is aimed primarily at students, researchers and postgraduates, as well as instructors, who are supported by additional training materials. However, it is also suitable for all those who use ML in practice and want to acquire more theoretical knowledge.
The book covers important developments in recent years such as the use of Bayesian methods in the mainstream.
However, practical exercises and project-based assignments are included as well. Prior knowledge of pattern recognition or machine learning concepts is not neccessary. However, basic knowledge of linear algebra and multivariate methods is recommended.
Tip: The workbook can be downloaded and used for free as a PDF from Microsoft.
“Artificial Intelligence: A Modern Approach” by Stuart J. Russell and Peter Norvig
This Artificial Intelligence book is also aimed primarily at students and is used as a textbook at over 1400 universities worldwide. However, it is generally suitable for those interested in AI, as it provides a clear and understandable introduction to the subject without staying too superficial. The book is even considered the world’s most popular textbook on artificial intelligence.
With a total of seven parts (and 27 subchapters), designed to be read in two semesters, it is a very comprehensive standard work. The fourth edition differs from previous versions by focusing on machine learning, deep learning, probabilistic programming and multiagent systems, and includes sections where the utility function of AI is uncertain.
It concludes with a discussion of the past and future of AI, how to define AI in the first place, and a discussion of various philosophical approaches. The programs in the book are portrayed in pseudocode, with implementations in Java, Python, and Lisp available online.
“Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems” by Martin Kleppmann
This book guides through the topics of data processing and storage. Here, challenges such as scalability, consistency, reliability, efficiency and maintainability must be solved – and there’s a huge variety of tools available which can be confusing.
Martin Kleppmann provides an overview by discussing the advantages and disadvantages of the technologies in a comprehensive and practice-oriented manner. He also highlights the basic principles behind them, which remain the same even if the software changes.
This makes the AI book particularly suitable for software engineers and architects who want to learn how to apply the concepts in practice and how to make the best use of data in modern applications. It also gives readers ideas on how to operate systems they are already using even more effectively.
“The Ultimate Data and AI Guide” by Alexander Thamm et al.
Artificial intelligence, machine learning and Big Data are much discussed and very complex topics – the “Ultimate Data and AI Guide”, co-authored by our founder Alexander Thamm, guides you through these fields and explains everything you really need to know about data and AI. The goal is to give the reader a complete overview and a solid understanding of the most important concepts around data, machine learning and AI.
The content is based on our experience from over 500 data projects in over 100 companies, explained in an understandable and practical way and arranged in 150 FAQs.
This makes it a suitable reading for (soon-to-be) Data Scientists who want to get a basic overview of the subject matter. But non-specialists as well will learn how AI, machine learning and data are increasingly shaping our economy and society – no prior knowledge is necessary. Lastly, the AI book can serve as a reference for experts. 63 case studies are included and provide ideas on how data and ML methods can be applied in your own company.
“Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World” by Marco Iansiti and Karim R. Lakhani
AI-centric companies operate differently than traditional ones, completely redefining how they invent, create, and deliver products and services. They are easily overcoming constraints such as scalability. This also means that completely different rules and probabilities apply to the strategy of such a company. “Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World” adresses this topic.
Marco Iansiti and Karim R. Lakhani explain how AI-centric companies are crossing industry boundaries and are producing products that continuously perform higher – to make even more accurate, complex and sophisticated predictions. They also describe new challenges and responsibilities for the C-suite of digital and traditional enterprises.
The read is aimed primarily at executives, but will be of interest to anyone who wants to understand how AI is changing business strategies.
“AI Superpowers” by Kai-Fu Lee
In addition to the basic AI books already mentioned, our Data Scientists recommend “AI Superpowers”. This is not about teaching theoretical basics, but a social and economic view of AI that is well worth reading.
Kai-Fu Lee, former CEO of Google China and one of the most renowned AI experts in the world, discusses the massive and unstoppable revolution that AI promises. Thanks to his background, he represents the Chinese-American perspective and paints a dramatic picture of developments in recent years and the future.
While the basic research of AI mainly happens in Europe and the U.S., China is clearly the revolutionary leader. Kai-Fu Lee describes the reasons for this development and reveals what distinguishes Chinese business culture from others – and why it produces such successful companies.