This book is still relevant today!
It describes many ML concepts, such as linear regression, neural networks, support vector machines, Gaussian processes, probabilistic graphical models, variational inference, and hidden Markov models, which are still relevant today. If you follow any decent course on ML, it should cover most of these topics. In fact, during one course on ML that I had at university (a few years ago), we used this book as a reference.
Clearly, this book does not contain the description of the latest state-of-the-art models (for example, transformers), but it's a decent book for introducing many concepts in ML.
So, if you want to get a good overall knowledge of ML, then you can surely start with this book (provided that you have a minimal mathematical background to understand the ML concepts).
You may also want to take a look at this post.