“Mastering Machine Learning Algorithms”
by Giuseppe Bonaccorso
The Review
Our exciting field of data science (DS) is exploding, and it’s hard to keep up with all of it. We each become extremely focused on our specific work in our current roles, and we are each funneled into specialized areas to solve specific challenges.
After a long battle to deliver your great DS tool to production, your next challenge arrives. It seems this problem will require a DS method that you haven’t used since college. Maybe you’re not even sure, which DS method will best address your problem. Regardless, once you decide on a method of modeling, you have limited time to master it. You still need to collect, refine, and condition your data for that method. You must also master how to fight through the training of your chosen model. You’d seek help from your fellow DS’s, but they’re in the situation you just left.
Now enters Giuseppe Bonaccorso – a DS friend with a corpus of DS methods that provide adequate mathematical overviews, explanations, and python code applied to substantial examples to get you up to speed quickly. I believe in keeping multiple sources at hand for learning / reviewing any methods. In that spirit, I’m relieved to have Giuseppe’s book in my library. In a mere 750+ pages, Giuseppe takes you on a tour of important methods that, while they won’t make you an expert in the foundational mathematics for each method, they won’t leave you blind in those respects either.
I especially appreciate the references to foundational papers for each method following every group of methods for when we might need or want to go deeper. If you estimate that your library could be enhanced by such a substantial book as I’ve described, I believe you’d be well benefited by Giuseppe’s book. He even manages to provide a good review of reinforced learning that, in my opinion, is extremely challenging to teach clearly.
An important note is that this book does not cover natural language processing. In my experience, NLP would require another 750+ pages if it were to be covered as well as the methods that Giuseppe has covered. However, this is the only major field that he skips, and he does relate areas of that field to the techniques that he covers.
