Requirements: .ePUB, .PDF reader, 53.1 MB
Overview: This book addresses the growing need for a comprehensive guide to the application of Machine Learning in financial analytics. It offers a valuable resource for both beginners and experienced professionals in finance and Data Science by covering the theoretical foundations, practical implementations, ethical considerations, and future trends in the field. It bridges the gap between theory and practice, providing readers with the tools and knowledge they need to leverage the power of Machine Learning in the financial sector responsibly. The financial world has always been a realm of complexity, marked by volatility, uncertainty, and dynamic interconnectedness. Traditional models and tools have often struggled to capture the multifaceted nature of this domain. However, Machine Learning techniques offer a paradigm shift, providing the capability to process vast amounts of data, identify patterns, and generate insights that were previously unimaginable. Throughout the chapters of this book, we explore the fundamental principles of Machine Learning and how they can be applied to tackle a myriad of financial challenges. From predictive modeling, risk assessment, algorithmic trading, portfolio optimization, fraud detection, to customer segmentation, the potential applications are boundless. Object-oriented programming in Python combined with the power of NumPy, Matplotlib and Jupyter fits the bill perfectly for design and visualization in financial engineering. We find that Python combined with Jupyter is not only very well suited for designing and visualizing structured products and examining the impact on pricing as different design elements are tweaked, but it is also amenable to a variety of extensions and integration with other open-source computational finance libraries.
Genre: Non-Fiction > Tech & Devices
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