Advances in Financial Machine Learning
Marcos Lopez de Prado
แแแ. 2018 ยท Gildan Media ยท แแแฎแ แแแแแ: Steven Jay Cohen
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Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Listeners will learn how to structure Big Data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; and how to backtest their discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Listeners become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.
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Dr. Marcos Lopez De Prado manages several multibillion-dollar funds for institutional investors using ML algorithms. Marcos is also a research fellow at Lawrence Berkeley National Laboratory. He has published dozens of scientific articles on ML in the leading academic journals, and he holds multiple international patent applications on algorithmic trading.
Steven Jay Cohen has been telling stories his whole life, and has worked professionally as a storyteller since 1991. A classically trained actor, he has worked both on stage and behind the microphone for most of his career. Born and raised in Brooklyn, Steven now resides in scenic western Massachusetts.
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แแแแแแกแขแแแแ แแ Google Play Books แแแ Android แแ iPad/iPhone แแแฌแงแแแแแแแแแแกแแแแก. แแก แแแขแแแแขแฃแ แแ แแแแแฎแแ แชแแแแแแก แกแแแฅแ แแแแแแชแแแก แแฅแแแแก แแแแแ แแจแแแ แแ แกแแจแฃแแแแแแก แแแแชแแแ, แฌแแแแแแฎแแ แกแแกแฃแ แแแแ แแแแขแแแขแ แแแแแกแแแแ แแแแแแแก, แ แแแแ แช แแแแแแ, แแกแ แฎแแแแแ แแจแ แ แแแแแจแ.
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