Inside the Machine Learning Interview: 151 Real Questions from FAANG and How to Answer Them

This is a very good book for beginner for system design as it balance out between going in details and explaining what is necessary. For serious ML project you might need more in depth book but this book would keep you focussed from interview perspective. You might read this book along with other ML books in order to gain maximum benefit

The only book you need to prepare for MLSD

I have a strong tech background, having previously worked at Apple, Google, Facebook, etc. Having decided to start interviewing again recently, I decided to load up on books to help me in preparing. This one is definitely one of the better ones, providing many of the questions that I have either asked when interviewing candidates or that have been asked of me in my own interviews. The book is concise, getting straight to the point in many areas and provides plenty of questions to help me study. Please note that it does require a certain level of fluency in ML and coding. Consequently, you'll want to be comfortable with those topics before taking on this book. But if you need an additional resource to help you in studying for ML interviews, this one does well.

I have been in the machine learning industry for five years and am currently at a senior level. This book is so far the best ML foundation books for interview preparation I have read. It not only covers many practical topics you may encounter at work, such as how to missing data, how to do feature engineering, but also covers advanced topics such as missing labels due to Apple's app tracking transparency rule and generative models. Thanks Shao for putting such awesome materials together. I highly recommend it to anyone who is preparing for machine learning interviews.

Highly recommended guide for anyone preparing for ML interviews. It’s great practical resource in both depth and breadth that I haven’t seen elsewhere. It covers a wide range of potential ML questions, (ranging from recommendation systems, ad ranking systems to language models), and is concise, highlighting the crucial knowledge you need without leading you down rabbit holes.