The books "The Elements of Statistical Learning" by Trevor Hastie, and "Advances in Financial Machine Learning" by Lopez De Prado are highly recommended books for ML. They both deal with machine learning algorithms, and the statistics involved in ML algorithms. De Prado's book deals with ML for finance while Trevor Hastie's book seems to be generic. However, if ML for finance is concerned, which book is preferred and what are other differences between these books if any?
As mentioned in your question, that "if ML for finance" is concerned, then I think De Prado's book should be preferred, since his book puts more emphasis on how to apply data science techniques to actual problems in finance. However, even with "Machine Learning" in the title, the book actually deals more with data analytics rather than concrete machine learning algorithms, so you might want to take that into consideration. Nonetheless, the book still outlines the basics of a prediction task: it tells you how to correctly define a prediction label, shows you some features to build a model, and how to use your prediction signals to trading. So at least you get a general framework of the process.
ESL covers a broad variety of topics and some of them are not practical in the financial domain. I would suggest you use it as a reference book when stuck with certain specific algorithms. If you are just starting to learn machine learning, then there's a simple version of ESL called An Introduction to ESL which you could quickly finish and move on to AFML. If you are already familiar with ML, just directly read Prado's book.