It has taken more than a year to properly digest the material of this book authored by world–renowned Dr. de Prado, an undisputed authority in his field of study. What I liked in particular is the crystal clear way of conveying the applications of ML methods to the respective fields in finance and their limitations, e.g. applying the fractional differencing to financial time series to maintain the stationarity while not compromising on memory, RANSAC method for outliers detection, introducing a novel Deflated Sharpe Ratio concept to account for controlling of experiments, hence, reestablishing rigorous mathematical standards in finance, a true characteristic befitting an academic discipline. And this is just the tip of the iceberg. Curious researcher may want to check out the list of peer reviewed scientific publications by Dr. de Prado to comprehend the research contribution he had already made and is still making to the field of Finance (one of the recent publications relates to exploratory causal analysis, a discipline at the intersection of experimental design, statistics and CS pertaining to learning cause and effect relationships).