Strengthen your career: Machine Learning in Finance. Expand your skills in the algorithms and tools required to predict financial markets.
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No prior knowledge required
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The main objective of this specialization is to provide the knowledge and practical skills required to develop a strong foundation in the core paradigms and algorithms of machine learning (ML), with a special emphasis on applications of ML to various practical problems in the field of finance. The specialization aims to help students be able to solve practical problems suitable for machine learning that may arise in everyday life, which include:
The modules can also be taken individually to improve relevant skills in a specific area of ML applications for finance.
The specialization focuses on machine learning, with all examples, homework assignments, and course projects dealing with various problems in the field of finance (such as stock trading, asset management, and banking applications), and the selection of topics is made with an emphasis on ML methods used by finance professionals. The specialization is designed to prepare students to work on complex machine learning projects in finance, which often require both a broad understanding of the entire field of ML, and an understanding of the various methodologies available in a specific field of ML (for example, unsupervised learning) to solve practical problems that may be encountered in their work.
The goal of this course is to provide an initial and broad overview of the field of machine learning (ML) with a focus on applications in finance. The final lesson uses supervised machine learning methods to predict bank closures. Although this course can be taken separately, it serves as an introduction to topics that will be covered in depth in the following modules in the “Machine Learning and Reinforcement Learning in Finance” training.
The goal of a guided tour of machine learning in finance is to understand what machine learning is, what it is used for, and how it can be applied to different financial problems.
Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory, and basic calculus is required to complete the assignments in this course.
This course is designed to help students solve practical problems suitable for machine learning that may arise in real life, which include: (1) understanding where their problem lies on the general map of available ML methods, (2) understanding which specific ML approaches would be best suited to solve the problem, and (3) being able to successfully implement a solution, and evaluate its performance. A student with little or no prior knowledge of machine learning (ML) will be exposed to the main algorithms of supervised learning, unsupervised learning, and reinforcement learning, and will be able to use open source Python packages to design, test, and implement ML algorithms in finance.
Machine Learning Fundamentals in Finance will provide a deeper look at supervised learning, unsupervised learning, and reinforcement learning, and will conclude with a project where unsupervised learning is used to implement a simple trading strategy.
Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory, and basic calculus is required to complete the assignments in this course.
This course is designed to introduce the basic principles of reinforcement learning (RL), and develop application cases for applying RL to options valuation, trading, and asset management. At the end of the course, students will be able to:
Prerequisites are the courses “Guided Tour of Machine Learning in Finance” and “Fundamentals of Machine Learning in Finance”. Students are expected to know about the log-normal process and how it can be symbolized. Knowledge of option valuation is not required but is desirable.
In the final course in our training, An Overview of Advanced Methods in Reinforcement Learning in Finance, we will delve deeper into the topics covered in our third course, Reinforcement Learning in Finance. In particular, we will discuss the connections between reinforcement learning, option pricing, and physics, the implications of reverse reinforcement learning for market impact modeling and price dynamics, and perception-action cycles in reinforcement learning. Finally, we will provide an overview of potential applications of reinforcement learning in high-volume trading, cryptocurrencies, peer-to-peer lending, and more.
After taking this course, students will be able to:
