The course is perfectly explained, but I would like more practical exercises to improve.
Which book can I use as support material?
Ideally, I would prefer a book that covers all the topics of the specialization or at least one book for each course.
Thanks a lot for your help!
Here’s a thread with some recommendations for Linear Algebra books.
One of the books recommended is from Professor Gilbert Strang at MIT. He is a prolific author and also has a book about calculus (M4ML C2). You can find references to his books on page 2 of this PDF of the preface from his Linear Algebra for Everyone book.
Of course Linear Algebra, Calculus and Multivariate Calculus are all standard college math courses, so there are literally hundreds of textbooks available on any of these subjects. Reading an actual math textbook on any of these subjects will be a lot more of a time commitment than taking M4ML.
Thanks Paul for the recommendations. I really appreciate your help.
I found this others book:
Both authored by Serge Lang.
Thank you for the additional recommendations. There are lots of math textbooks out there! 
Part 1 Chapter 2 of the free and popular Deep Learning Book may also help you, as well as the Mathematics for Machine Learning Book.
@javila though I’ve been a little slow getting fully up to speed with it, I’ve also been reading Anil Ananthaswamy’s recently released Why Machines Learn by Anil Ananthaswamy: 9780593185742 | PenguinRandomHouse.com: Books.
I tag @paulinpaloalto here only because it is less strictly a math book, but sometimes I quite enjoy the history behind topics (or ‘how on Earth did we get here ?’)-- Even the clear situation and setting of the discovery/invention of the Perceptron alone is worth the price of admission IMHO.
Unfortunately a pure math’s text won’t give you context, or ‘what were they thinking (?!)’. But I second Paul’s Stang recommendation.