The data science and machine learning (ML) industry is growing in maturity with developments in MLOps and more machine learning models being deployed in production every day. It’s therefore important to not only stay up to date with current and developing trends, but also review your basics every now and then to refresh your knowledge. To understand how machine learning can be applied in practice, you can check out this ML guidebook and read through our numerous ML-centered blog posts.
As you can tell by our many ML resources, machine learning is a dense subject that can be analyzed and used in many different ways, from many different angles. After reading Dataiku's take on ML in practice, you might want to go back even further to the basics. To do so, I recommend “The Hundred-Page Machine Learning Book,” written by Andriy Burkov. Published in 2019, this concise and to-the-point book can be read in one day and will refresh your memory on modern machine learning concepts.
Who Is This Book For?
This book goes over everything regarding the development of machine learning (since 1960) that has proven to have a significant practical value. It's very clear structure which divides each topic into chapters, sections, and subsections, enables the book to treat every subject matter in just a few paragraphs or pages. From types of machine learning to data structures and fundamental algorithms to deep learning, this guidebook covers practically every aspect of machine learning in the most efficient way possible.
Beginners will find just enough details to get a comfortable level of understanding and start asking the right questions. Data scientists will benefit from directions for further self-improvement. Furthermore, practitioners can use this book when brainstorming at the beginning of a project or anytime they need a quick refresher on a technique, term, or theory.
However, while the book can serve as a quick intro to ML, at times, it will skip the explanatory stage and dive into high-level details (that are difficult to understand for beginners). For example, the chapter on Advanced Practice is not very accessible to individuals without any background knowledge. I thus recommend this book to beginner data scientists and other technical roles in search of a handy guidebook that contains all the essentials of machine learning.
Meet Your New Companion Wiki!
As mentioned, each section of this book is rather brief, as it focuses on the essentials. So what can you do if you want to learn more? This guidebook anticipates your thirst for knowledge as it includes QR codes that can be scanned with your phone to lead you to the book’s companion wiki. This wiki has additional material including recommended reads, videos, Q&As, code snippets, and more! Plus, the book’s wiki is continuously updated with contributions from the book’s author as well as volunteers around the world. So this book will always have something new to teach you!
“The Hundred-Page Machine Learning Book” is not only a book you can read in a day, but a reference you can keep on your desk whenever you need to review a term, theory, formula, etc (or discover the updates made in the companion wiki!). Think of it as your very own machine learning dictionary! Just open the book to the section that interests you for a quick overview and then look for the Dataiku ebook that can help you dive deeper and see how you can implement machine learning in your work with the right tools.