I thought the book was better than a lot of stuff out there, although I did find it rather odd how he only used classification and completely ignored regression. Personally I have found there is a lot of information loss when using classification especially at non-HFT time scales. Maybe the info in the book is more useful with the assets he trades but it's definitely missing some necessary steps if trying to do ML in equities which is what I trade.
So the large body of research regarding financial machine learning is related to classification rather than regression. The typical regression models used are CAPM and APT related, and a large amount of those use linear models, very dated. The financial ML papers out there focused on regression are pretty sparse.
Re Kevin's comment: de Prado just won Quant of the year 2019, is the leading researcher in financial ML and if anything he probably has a Nobel Prize waiting for him in the future. He is the head of ML at AQR, has made millions of dollars for Guggenheim Partners, did his Postdoctoral research at Harvard. I don't think a more able and credible person exists. Kevin is of course entitled to his own opinion.

