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A01=Guangxi Yu (Sws Research
A01=Hao Ni (Univ College London
A01=Jinsong Zheng (Huatai Securities
A01=Xin Dong (Citadel Securities Llc
Author_Guangxi Yu (Sws Research
Author_Hao Ni (Univ College London
Author_Jinsong Zheng (Huatai Securities
Author_Xin Dong (Citadel Securities Llc
Category=KFF
China)
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Financial Case Study
Linear Models
Machine Learning
Neural Network
Python
Quantitative Finance
Reinforcement Learning
Statistics
Supervised Learning Algorithms
Tree-Based Models
Uk)
Un-Supervised Learning

Product details

  • ISBN 9781786349361
  • Publication Date: 03 Jan 2021
  • Publisher: World Scientific Europe Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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In today's world, we are increasingly exposed to the words 'machine learning' (ML), a term which sounds like a panacea designed to cure all problems ranging from image recognition to machine language translation. Over the past few years, ML has gradually permeated the financial sector, reshaping the landscape of quantitative finance as we know it.An Introduction to Machine Learning in Quantitative Finance aims to demystify ML by uncovering its underlying mathematics and showing how to apply ML methods to real-world financial data. In this book the authorsFeatured with the balance of mathematical theorems and practical code examples of ML, this book will help you acquire an in-depth understanding of ML algorithms as well as hands-on experience. After reading An Introduction to Machine Learning in Quantitative Finance, ML tools will not be a black box to you anymore, and you will feel confident in successfully applying what you have learnt to empirical financial data!The Python codes contained within An Introduction to Machine Learning in Quantitative Finance have been made publicly available on the author's GitHub: https://github.com/deepintomlf/mlfbook.git

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