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: Paperback / softback
\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.

In addition to its theoretical aspects, An Introduction to Machine Learning in Quantitative Finance provides practical insights and applications of ML in the financial industry. The authors showcase real-world case studies and examples that demonstrate how ML algorithms can be used to solve complex financial problems. From risk assessment and portfolio optimization to fraud detection and market analysis, this book covers a wide range of ML applications, providing readers with a practical roadmap for implementing ML in their own work.

Another notable feature of An Introduction to Machine Learning in Quantitative Finance is its extensive Python code repository. The authors have made the Python codes contained within the book publicly available on their GitHub repository, https://github.com/deepintomlf/mlfbook.git. This allows readers to follow along with the examples and experiment with ML techniques on their own computers. The code is well-documented and easy to understand, making it an invaluable resource for anyone who wants to deepen their understanding of ML and apply it in their work.

In conclusion, An Introduction to Machine Learning in Quantitative Finance is a must-read for anyone interested in leveraging the power of ML to solve financial problems. With its comprehensive coverage of ML concepts, practical applications, and extensive Python code repository, this book provides readers with the knowledge and tools they need to excel in this rapidly evolving field. Whether you are a financial professional, data scientist, or simply someone passionate about learning and applying new technologies, this book will empower you to unlock the full potential of ML and make a significant impact in your work. So, why wait? Grab a copy of An Introduction to Machine Learning in Quantitative Finance today and embark on a journey of discovery and innovation!

\n Weight: 388g\n
Dimension: 152 x 230 x 17 (mm)\n
ISBN-13: 9781786349644\n \n