To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. By the end of the course, you will be able to use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks. This course will help you gauge how well the model generalizes its learning, explain the differences between regression and forecasting, and identify the steps needed to create development and implementation backtesters. You will learn how to identify the profit source and structure of basic quantitative trading strategies. In this course, you’ll learn about the fundamentals of trading, including the concept of trend, returns, stop-loss, and volatility. To successfully complete the exercises within the program, you should have advanced competency in Python programming and familiarity with pertinent libraries for Machine Learning, such as Scikit-Learn, StatsModels, and Pandas a solid background in ML and statistics (including regression, classification, and basic statistical concepts) and basic knowledge of financial markets (equities, bonds, derivatives, market structure, and hedging). This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading. By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies. This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning (ML) and Python.
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