Top Data Science and Machine Learning Books to Read in 2023 | by Dan Pietrow | May, 2023


Stay ahead of the curve: learn and grow with these distinguished data science and machine learning books

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As a recent Forbes publication highlighted, continuous learning is one of the most effective strategies to propel your career forward.

This advice rings especially true in the evolving realms of data science and machine learning.

Over the past year, we’ve witnessed amazing advancement in the field of artificial intelligence (AI), most notably with the release of ChatGPT. These breakthroughs are a constant reminder of the industry’s fast pace and the importance of continuous personal development.

ChatGPT OpenAI image
Photo by Levart_Photographer on Unsplash

Today there is such a vast ocean of data science resources that finding good learning content can feel like a never-ending task.

It’s easy to get swamped and lose your motivation.

I’ve compiled a curated list of books to help prevent this and save you some precious time. These gems provided me with invaluable insights during my own data science journey, and I’m confident they’ll do the same for you.

Following the popularity of a similar list I shared last year, I decided to spruce it up and create a fresh list for 2023. These books cater to a wide audience: from data science rookies to seasoned practitioners and business executives looking to gain a deeper understanding of data science and AI.

If you are short on time and can’t read the whole blog, let me spotlight Hands-On Machine Learning by Aurélien Géron. Although this one requires some Python knowledge, there is no other book I have found myself returning to as often while practising data science. A true gem.

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Now without further ado, here is the full list. Enjoy!

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e: Concepts, Tools, and Techniques to Build Intelligent Systems

by Aurélien Géron

First up, we have what I consider the best book on machine learning. Clear, concise, and practical, this book offers a great hands-on approach to studying machine learning. By using popular Python packages, the author helps the reader develop project-based technical skills.

Despite being very practice-oriented, the theory is still well-covered without overwhelming the reader with complex mathematical equations.

The target audience is from beginner to advanced; a great guide for beginners and a great reference point for experts. However, a decent grasp of the Python programming language is definitely recommended.

The unique feature of this book is that the end of each chapter has exercises to help you apply what you’ve learned. This book can help you prepare for your first job or a new project.

Chapter two, “End-to-End Machine Learning Project”, helped me through all my first data science projects when I first purchased this book. 2nd edition back then, the new 3rd edition has now found its way onto my desk. To this day, every time I start a new machine-learning problem it’s my first reference point if I need a refresher.

You can find this book on Amazon.

Naked Statistics: Stripping the Dread from the Data

by Charles Wheelan

My most recent read, Naked Statistics, is an entertaining and practicable book. It could even fall under the holiday-read category. Full of real-life examples, this is a great option for those who don’t have a mathematical background and might find complex mathematical formulas and explanations intimidating.

Via a fun narrative, Charles Wheelan will help you grasp the fundamental understanding of statistics, probability, inference, and many other topics.

The books mentioned above help you cover hands-on coding skills and machine learning theory. Naked Statistics can help you understand and interpret data. A crucial skill for all data scientists.

Reading this book eased my maths imposter syndrome. Wheelan’s engaging approach and use of relatable real-life examples transformed my view of statistics. It also helped me further avoid procrastinating learning this subject.

You can find this book on Amazon.

Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD

by Jeremy Howard and Sylvian Gugger

If you’re an experienced data scientist and you still haven’t heard of the fast.ai community, you’re missing out!

Fast.ai is a goldmine.

Not only do they have an excellent Python library for building deep learning models in an accessible way, but they also have perhaps the best hands-on course on deep learning for coders. And it’s totally free!

In addition, they offer a FREE book written in Jupyter Notebooks that can also be purchased in a print edition. This book even includes a chapter on using traditional machine-learning algorithms to work with tabular data.

Jeremy Howard demonstrates with both his course and book that you don’t need to be a PhD mastermind to do deep learning. On the contrary, the subject is a lot more approachable. The top-down approach in this book allows you to work on the practical side of machine learning before going deeper into theory if you want to.

The unique feature of this book is that it makes you great at the fundamentals. While new algorithms and tools like ChatGPT will continue to be developed, the fundamentals will likely stay the same for much longer. Mastering fundamentals is key; with this book in hand, you’ll have the chance to do that.

The combination of the fastai course and book proved invaluable to my PhD work as I learned to experiment faster with deep learning.

You can find this book on Amazon.

Storytelling with Data: A Data Visualization Guide for Business Professionals

by Cole Nussbaumer Knaflic

So far, the books on this list have been focused on the hard skills of data science. But in this field, soft skills can be just as crucial. One of these is the ability to communicate results to different stakeholders in a clear fashion.

As the book title suggests, you need to become a storyteller. When you finish a data science project, it’s not all about showing your data and results and leaving it there. It’s crucial to understand where to draw the receivers’ attention and make your data story easy to follow and understand.

In this book, Cole Knaflic, who used to work as a data analyst at Google, gives away useful tips for building perfect charts.

Anyone who works with data can benefit from reading this book. Whether you are a student working on a thesis, an employed data scientist, or a manager communicating in a data-informed way.

My biggest takeaway from this book was about decluttering my charts. This simple tip allows viewers to focus more on the data, for example, by using minimal borders when presenting a table.

You can find this book on Amazon.

Machine Learning Yearning

by Andrew Ng

By the founder of Coursera, deeplearning.ai, and Google Head, Machine Learning Yearning is another book on this list that can be read in one day.

Packed with experience and wisdom, this timeless FREE book is a must-read for anyone in the AI community.

Andrew Ng has a great ability to digest complex concepts into simple ones. In this book, he advises on how to approach projects, split your data, and improve model performance.

The book is perhaps most useful for beginners who have completed a few personal projects. What’s unique about this book is that it covers the strategy for approaching machine learning problems. And it can serve as a great guide when working on a new project with a team.

Reading this book clarified the bias-variance tradeoff for me. This guided my decision-making on where to invest more hours when not seeing the expected model performance. In a bullet-point fashion, Andrew Ng even offers suggestions for addressing each scenario of high bias and high variance.

You can download the book for free here: deeplearning.ai

Life 3.0: Being Human in the Age of Artificial Intelligence

by Max Tegmark

If you’re intrigued by the future of artificial intelligence, then this book is your key to joining the conversation. Life 3.0, one of three books Bill Gates credits with shaping his thinking around AI, is an enlightening exploration of this technology’s potential impact on society.

Written by an MIT professor, this non-fiction piece is thought-provoking and accessible to all readers. It delves into how AI could revolutionise our jobs, society, and the very sense of being human. Going forward, these technological developments will need careful ethical consideration and it’s up to us to determine whether they will have a positive or negative impact.

This book helped me appreciate the importance of ethical considerations surrounding AI and my responsibility as a creator of machine learning tools.

You can find this book on Amazon.

by Foster Provost and Tom Fawcett

Data is valuable. But you must understand how to leverage it to give your company a competitive advantage. Data Science for Business avoids equations and focuses on the foundations of data science. If you are a manager or developer and want to enhance your data literacy, this book is for you. It enables you to understand how data science fits within your organization and how it can be harnessed to better your business.

Finally, the author will help build your data-analytical thinking through many real-life examples and bridge the communication gap with data scientists.

While more suited for business executives, this book was still valuable to me as it helped me better understand how to build successful data science projects and teams.

You can find this book on Amazon.

Continuous learning is important if you want to stay ahead in your career. With the wealth of learning material out there today, it can be hard to pick a good book or course.

While online courses are great, books let you learn at your own pace and are often more in-depth.

The books on this list have all helped me in my career. They’ve served as reference points when embarking on new projects and have assisted me with getting my first data science job. Hopefully, these books will prove just as useful to you on your journey!



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