Top 10 Data Science and AI #Books for 2021

 Top 10 Data Science and AI #Books for 2021




Here are some of the notable data science publications that are currently available in print form, listed in order of publication. 

I have found myself using more and more online training resources, blogs, videos, and other sources of information besides printed books for keeping up with industry trends and the need to up-skill in a variety of topics. 

It seems we aren’t quite ready to give up books yet, and so in some cases, the same content creators have now added print books to their offerings.

The books below cover topics ranging from mathematics, to business strategy, to model building and implementation, to societal impact of artificial intelligence. 

They are available on Amazon or can be ordered from your favorite local bookstore. 

I selected books that had larger number of reviews on Amazon, were published since the beginning of 2020, and covered interesting subjects related to data science, engineering, and AI. They are all also available in digital formats.

The Book List

Iansiti, Marco, and Karim Lakhani. Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World. Harvard Business Review Press, 2020.

  • This book focuses on the acceleration of data-driven decision making in conjunction with digital transformation of brick-and-mortar business models. This includes AI models as well as the more traditional inventory forecasting that suddenly became mission critical in the age of Covid-19.

Thompson, John and Douglas Laney. Building Analytics Teams: Harnessing analytics and artificial intelligence for business improvement. Packt Publishing, 2020.

  • This book explores the different ways to structure the management of a data science team within the organization, and the various ways that senior management can impact the success of the people and projects. The mindset necessary to succeed with innovation projects is quite different from other business initiatives and requires the right leaders at the top of the chart.

Deisenroth, Marc Peter. Mathematics for Machine Learning. Cambridge University Press, 2020.

  • The title sums it up for this one, if you are unfamiliar with the mathematics behind statistical and machine learning methods and determined to learn, this will help you out with the fundamentals. Topics include linear algebra, calculus, and probability functions underlying linear regression, principal component analysis, gaussian mixture models, and support vector machines.

Burkov, Andriy. Machine Learning Engineering. True Positive Inc, 2020.

  • A guide to practical applied machine learning model development and implementation. Many otherwise good data scientists may lack experience with implementation, or even consider it “out-of-scope” for model developers. The best practices and design principles contained are usually learned on the job through involvement in all phases of the project life cycle. The book is a follow up to The One Hundred Page Machine Learning Book.

Howard, Jeremy and Sylvain Gugger. Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD. O’Reilly Media, 2020.

  • PyTorch is gaining rapidly in popularity for deep learning model development, and this book will get you going with computer vision, NLP and more, using fastai. The book complements their on-line course and code, and has a goal of making building deep learning models achievable by a broader field of practitioners than research scientists.

Moroney, Laurence. AI and Machine Learning for Coders: A Programmer’s Guide to Artificial Intelligence. O’Reilly Media, 2020.

  • An introduction to TensorFlow for images, NLP and more from Google, including sections on TensorFlow Lite for mobile deployments. The book is a good complement to the TensorFlow specialization at Coursera.

Morales, Miguel. Grokking Deep Reinforcement Learning. Manning Publications, 2020.

  • Part of the “Grokking” series — tacking reinforcement learning. The author is affiliated with Georgia Tech and their reinforcement learning course as well as the Udacity nanodegree and now gives this introduction to the application of deep reinforcement learning models. This book is for machine learning modelers who want to add reinforcement learning to their toolkit.

Lakshmanan, Valliappa, Sara Robinson, and Michale Munn. Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps. O’Reilly Media, 2020.

  • If you are not from an engineering background, the concept of machine learning design patterns may be a new one. But it is also a great idea to define common patterns and repeatable solutions, which we are all familiar with if you work with modularized code. Lak is familiar as well from the Google Cloud Platform courses on Coursera, and with his Google coauthors has put out this explanation of best practices and practical solutions.

Harford, Timothy. The Data Detective: Ten Easy Rules to Make Sense of Statistics. Riverhead Books, 2021.

  • From the author of the bestseller The Undercover Economist, we have this explanation of how statistics can help us know what and what not to believe, and how statistics can be a tool to help us understand the world. Our emotional responses to information can bias how we process and respond to inputs, such as dismissing evidence we do not care to hear. This books seems very timely in light of the recent political climate.

Rochwerger, Alyssa Simpson and Wilson Pang. Real World AI: A Practical Guide for Responsible Machine Learning. Lioncrest Publishing, 2021.

  • Real world examples of AI in industry and how it can go wrong, with easy to read and non-technical explanations from technical product leaders. The concepts of bias and responsible usage of AI are parts of data literacy that business leaders will need to master as usage of models becomes more widespread.

Amos, David, Dan Bader, Joanna Jablonski, and Fletcher Heisler. Python Basics: A Practical Introduction to Python 3. Real Python, 2021.

  • A book from the realpython.com team, who also put out popular tutorials for learning python that help beginners get up to speed as quickly as possible.

Bonus Book

Larson, Erik. The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do. Belknap Press, 2021.

  • This one is hot off the presses this week! It is a discussion of general human intelligence versus the current state of artificial intelligence, and how progress in a narrowly defined, specialized area (how to play chess) does not necessarily mean we are getting closer to human-like thinking machines. So, take a rain-check on the impending arrival of the robot overlords, that is going to have to wait a while.

Summary

I hope this inspires you to get reading! Good luck and I hope you enjoy reading one (or more) of these books as you continue your journey in data science and engineering.



#MachineLearning #IIoT #Python #RPA #AI #100DaysOfCode #DL #DevOps #Analytics #NLP #writing #cybersecurity #IoT #DataScience #startup #RStats #javascripts



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