You want to read either Elements of Statistical Learning (2nd ed.) OR Kevin Murphy's ML book for the theory.
Then you will want to consult a text book in your work domain (e.g. introduction to speech & language OR statistical natural language processing for the domain of natural language processing).
And finally, you will want either a book, or free online Web resources/tutorial videos that show you how to do things in practice, given a particular programming language and tool-set (e.g. Python + TensorFlow, Java + DeepLearing4J).
This recipe of Theory + Application + Practice/Tools should get you there.
BTW, it is okay to read stuff in parallel, and to take a non-linear approach to learning. Also people have different preferences for textbook styles, and they vary regarding pre-existing background knowledge.
Then you will want to consult a text book in your work domain (e.g. introduction to speech & language OR statistical natural language processing for the domain of natural language processing).
And finally, you will want either a book, or free online Web resources/tutorial videos that show you how to do things in practice, given a particular programming language and tool-set (e.g. Python + TensorFlow, Java + DeepLearing4J).
This recipe of Theory + Application + Practice/Tools should get you there.