Machine Learning: Algorithms for Business

Machine Learning: Algorithms for Business

Marcos Lopez de Prado shares what is often hidden – the most profitable machine learning algorithms he has used over two decades to manage large pools of capital from the most demanding investors.
Machine learning is changing virtually every aspect of... our lives; ML algorithms perform tasks that until recently were entrusted only to proven experts. In the near future, machine learning will dominate finance, fortune-telling will fade into the past, and investments will cease to be synonymous with gambling.
Take the opportunity to be part of the “machine revolution”; all it takes is to get acquainted with the first book that provides a complete and systematic analysis of machine learning methods relevant to finance: from the structure of financial data, labeling financial time series, weighting samples, differentiating time series... to a whole section dedicated to proper backtesting of investment strategies.

Author: Маркос Лопес де Прадо

Printhouse: piter

Series: IT for Business

Age restrictions: 16+

Year of publication: 2019

ISBN: 9785446111541

Number of pages: 432

Size: 233x165x18 mm

Cover type: soft

Weight: 550 g

ID: 861646

Marcos Lopez de Prado shares what is often hidden – the most profitable machine learning algorithms he has used over two decades to manage large pools of capital from the most demanding investors.
Machine learning is changing virtually every aspect of our lives; ML algorithms perform tasks that until recently were entrusted only to proven experts. In the near future, machine learning will dominate finance, fortune-telling will fade into the past, and investments will cease to be synonymous with gambling.
Take the opportunity to be part of the “machine revolution”; all it takes is to get acquainted with the first book that provides a complete and systematic analysis of machine learning methods relevant to finance: from the structure of financial data, labeling financial time series, weighting samples, differentiating time series... to a whole section dedicated to proper backtesting of investment strategies.