Introduction To Machine Learning In Quantitative Finance, An

Machine learning (ML) is increasingly used in quantitative finance to reshape the landscape of quantitative finance. This book aims to demystify ML by uncovering its underlying mathematics and showing how to apply ML methods to real-world financial data. The Python codes contained within the book have been made publicly available on GitHub.

\n Format: Hardback
\n Length: 264 pages
\n Publication date: 03 January 2021
\n Publisher: World Scientific Europe Ltd
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In today's ever-evolving world, we are constantly exposed to the intriguing term "machine learning" (ML), which promises to be a magical solution for a wide range of problems, spanning from image recognition to machine language translation. Over the past few years, ML has stealthily made its way into the financial sector, reshaping the landscape of quantitative finance as we know it.

An Introduction to Machine Learning in Quantitative Finance is a comprehensive guide designed to demystify ML and unravel its underlying mathematics, making it accessible to anyone interested in delving into this field. This book strikes a perfect balance between mathematical theorems and practical code examples, providing readers with a deep understanding of ML algorithms and hands-on experience. Whether you are a seasoned financial professional or a newcomer seeking to expand your knowledge, this book will empower you to confidently apply ML techniques to real-world financial data.

One of the key strengths of An Introduction to Machine Learning in Quantitative Finance is its clear and concise writing style. The authors have a talent for explaining complex concepts in a straightforward manner, making it easy for readers to grasp the intricacies of ML. Each chapter is well-organized, with clear headings and subheadings that guide you through the material. Additionally, the book includes numerous illustrations and diagrams to enhance understanding and visual appeal.

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. This allows readers to explore the code in depth, gain a deeper understanding of the algorithms, and apply them to their own financial data analysis. The author's commitment to open-source learning is a testament to their dedication to advancing the field of ML and making it accessible to a wider audience.

In conclusion, An Introduction to Machine Learning in Quantitative Finance is a must-read for anyone interested in leveraging ML to solve financial problems. Whether you are a seasoned quantitative analyst or a beginner looking to expand your skills, this book will provide you with the knowledge and tools you need to succeed in this rapidly evolving field. So, why wait? Start your journey into the world of ML today and unlock the full potential of this powerful technology!

\n Weight: 526g\n
Dimension: 158 x 237 x 22 (mm)\n
ISBN-13: 9781786349361\n \n