Advanced Machine Learning Algorithms for Complex Financial Applications is popular PDF and ePub book, written by Irfan, Mohammad in 2023-01-09, it is a fantastic choice for those who relish reading online the Business & Economics genre. Let's immerse ourselves in this engaging Business & Economics book by exploring the summary and details provided below. Remember, Advanced Machine Learning Algorithms for Complex Financial Applications can be Read Online from any device for your convenience.
Advanced Machine Learning Algorithms for Complex Financial Applications Book PDF Summary
The advancements in artificial intelligence and machine learning have significantly affected the way financial services are offered and adopted today. Important financial decisions such as investment decision making, macroeconomic analysis, and credit evaluation are becoming more complex within the field of finance. Artificial intelligence and machine learning, with their spectacular success accompanied by unprecedented accuracies, have become increasingly important in the finance world. Advanced Machine Learning Algorithms for Complex Financial Applications provides innovative research on the roles of artificial intelligence and machine learning algorithms in financial sectors with special reference to complex financial applications such as financial risk management in big data environments. In addition, the book addresses broad challenges in both theoretical and application aspects of artificial intelligence in the field of finance. Covering essential topics such as secure transactions, financial monitoring, and data modeling, this reference work is crucial for financial specialists, researchers, academicians, scholars, practitioners, instructors, and students.
Detail Book of Advanced Machine Learning Algorithms for Complex Financial Applications PDF
- Author : Irfan, Mohammad
- Release : 09 January 2023
- Publisher : IGI Global
- ISBN : 9781668444856
- Genre : Business & Economics
- Total Page : 316 pages
- Language : English
- PDF File Size : 18,5 Mb
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