Course on Machine Learning for Trading by Google Cloud and NYIF [1 Month, Online Classes]: Enroll Now!

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About the Course

This Specialization 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. Alternatively, this specialization can be for machine learning professionals who seek to apply their craft to quantitative trading strategies.

The courses will teach you how to create various trading strategies using Python. By the end of the Specialization, you will be able to create long-term trading strategies, short-term trading strategies, and hedging strategies.

To be successful in this Specialization, you should have a basic competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL will be helpful. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).

There are 3 Courses in this Specialization

  • Introduction to Trading, Machine Learning & GCP
  • Using Machine Learning in Trading and Finance
  • Reinforcement Learning for Trading Strategies

Instructors

  • Jack Farmer Curriculum Director New York Institute of Finance
  • Ram Seshadri Machine Learning Consultant Google Cloud Platform

To enroll for this course, click the link below.

Course on Machine Learning for Trading

Note: NoticeBard is associated with Coursera through an affiliate programme.

Disclaimer : We try to ensure that the information we post on Noticebard.com is accurate. However, despite our best efforts, some of the content may contain errors. You can trust us, but please conduct your own checks too.

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