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Advances in Financial Machine Learning

Advances in Financial Machine Learning

28 in stock

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SKU 9781119482086 Categories General Computing & IT, Products

Learn to understand and implement the latest machine learning innovations to improve your investment performance Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that – until recently – only expert humans could perform. And finance is r...

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Description
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Description

Product ID:	9781119482086
Product Form:	Hardback
Country of Manufacture:	US
Title:	Advances in Financial Machine Learning
Authors:	Author: Marcos Lopez de Prado
Page Count:	400
Subjects:	Investment and securities, Investment & securities, Machine learning, Machine learning
Description:	Learn to understand and implement the latest machine learning innovations to improve your investment performance Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that – until recently – only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest. In the book, readers will learn how to: Structure big data in a way that is amenable to ML algorithmsConduct research with ML algorithms on big dataUse supercomputing methods and back test their discoveries while avoiding false positives Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.
Imprint Name:	John Wiley & Sons Inc
Publisher Name:	John Wiley & Sons Inc
Country of Publication:	GB
Publishing Date:	2018-05-04

Additional information

Weight	790 g
Dimensions	160 × 239 × 32 mm

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