Coursera – Machine Learning for Trading Specialization
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Start Your Career in Machine Learning for Trading. Learn the machine learning techniques used in quantitative trading.
What you’ll Learn:
• Understand the structure and techniques used in machine learning, deep learning, and
reinforcement learning (RL) strategies.
• Describe the steps required to develop and test an ML-driven trading strategy.
• Describe the methods used to optimize an ML-driven trading strategy.
• Use Keras and Tensorflow to build machine learning models.
Skills you’ll Gain:
• Finance
• Trading
• Investment
• Python Programming
• Machine Learning
• Machine Learning applied to Finance
• Algorithmic Trading
• Reinforcement Learning Model Development
• Reinforcement Learning Trading Algorithm Optimization
• Reinforcement Learning Trading Strategy Development
• Reinforcement Learning Trading Algo Development
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. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies.
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. As a challenge, you’re invited to apply the concepts of Reinforcement Learning to use cases in Trading.
This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level. 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). Experience with SQL is recommended.
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