Machine Learning (ML) is one of the fastest growing fields today. Financial Institutions continue to implement ML solutions to understand how markets work, access data, and forecast trends.

In the new Machine Learning and Reinforcement Learning Finance Specialization from New York University, you’ll learn the algorithms and tools needed to predict financial markets and how to use ML to solve problems.    Whether you’re a data scientist who wants to break into the finance industry or someone who currently works in finance, this Specialization will help you gain the knowledge and skills you need to develop a strong foundation. Instructor Igor Halperin, who has worked at both JP Morgan Chase and Bloomberg LP, gives us a sneak peek into what you’ll learn below: I started in theoretical physics and eventually, that led me to my first job in finance as a quantitative researcher focused on models of option pricing and credit risk modeling.

I started using various methods of non-parametric statistics for my models, and then slowly migrated into machine learning methods. I self-educated which is why I wanted to help create this Specialization because I know what it’s like to learn on your own.

This course is designed for students of finance or those who already work in finance who want to further their career by growing their skillset. Before taking this Specialization, learners should have a decent understanding of basic math including calculus and linear algebra, basic probability theory and statistics, and some programming skills in Python.

People who already know Machine Learning and/or Reinforcement Learning and want to learn about their applications in finance may also benefit from taking this Specialization. Absolutely. Read more from blog.coursera.org…

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