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Machine Learning for Finance. Build Next-Gen AI Systems for Trading, Forecasting, Risk, and Compliance Ahmed Menshawy, Mahmoud Fahmy, Ajaz Siddiqui

(ebook) (audiobook) (audiobook) Język publikacji: angielski
Machine Learning for Finance. Build Next-Gen AI Systems for Trading, Forecasting, Risk, and Compliance Ahmed Menshawy, Mahmoud Fahmy, Ajaz Siddiqui - okladka książki

Machine Learning for Finance. Build Next-Gen AI Systems for Trading, Forecasting, Risk, and Compliance Ahmed Menshawy, Mahmoud Fahmy, Ajaz Siddiqui - okladka książki

Machine Learning for Finance. Build Next-Gen AI Systems for Trading, Forecasting, Risk, and Compliance Ahmed Menshawy, Mahmoud Fahmy, Ajaz Siddiqui - audiobook MP3

Machine Learning for Finance. Build Next-Gen AI Systems for Trading, Forecasting, Risk, and Compliance Ahmed Menshawy, Mahmoud Fahmy, Ajaz Siddiqui - audiobook CD

Autorzy:
Ahmed Menshawy, Mahmoud Fahmy, Ajaz Siddiqui
Wydawnictwo:
Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:
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The integration of machine learning into finance is driving a fundamental shift—optimizing risk, forecasting markets, and enabling smarter compliance. Machine Learning for Finance is a hands-on, end-to-end guide designed to help readers bridge the gap between AI research and real-world financial deployment. Written by two leading AI engineers at Mastercard, this book combines deep technical insight with practical finance-specific workflows.

Starting with the foundations of ML, GenAI, and financial data, the book builds toward sophisticated applications such as agentic AI, reinforcement learning, and MLOps for finance. You'll learn to generate synthetic data using GANs, extract insights from SEC filings using transformers, and deploy real-time trading bots with monitoring pipelines. Each chapter includes projects modeled after real use cases—fraud detection, risk modeling, portfolio optimization, and more.

Machine Learning for Finance is tailored for practitioners. It goes beyond code snippets and theory, offering full-stack implementations that scale in production environments. Whether you're automating regulatory filings or building robo-advisors, this book gives you the tools to deliver AI innovation in one of the most complex, regulated industries.

O autorach książki

Ahmed Menshawy is a Research Engineer at the Trinity College Dublin, Ireland. He has more than 5 years of working experience in the area of ML and NLP. He holds an MSc in Advanced Computer Science. He started his Career as a Teaching Assistant at the Department of Computer Science, Helwan University, Cairo, Egypt. He taught several advanced ML and NLP courses such as ML, Image Processing, and so on. He was involved in implementing the state-of-the-art system for Arabic Text to Speech. He was the main ML specialist at the Industrial research and development lab at IST Networks, based in Egypt.
Mahmoud Fahmy is a Sr Machine Learning Engineer at Mastercard, specializing in the development and operationalization of AI products. His primary focus is on optimizing machine learning pipelines and navigating the intricate challenges of deploying models effectively for end customers.
Ajaz Ahmed Siddiqui is part of Microsoft's AI business solutions group, where he helps CFOs and finance teams bring AI agents into their core financial processes. Before Microsoft, he worked at Oracle, helping FP&A leaders implement budgeting, forecasting, and consolidation. Over the past decade, he has worked alongside finance leaders through successive waves of change, from early ML use cases in forecasting and anomaly detection to today's agentic AI handling end-to-end financial workflows.

Ahmed Menshawy, Mahmoud Fahmy, Ajaz Siddiqui - pozostałe książki

Packt Publishing - inne książki

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