About this Specialization
100% online courses

100% online courses

Start instantly and learn at your own schedule.
Flexible Schedule

Flexible Schedule

Set and maintain flexible deadlines.
Intermediate Level

Intermediate Level

Basic math including calculus and linear algebra, basic probability theory and statistics, and programming skills in Python.

Hours to complete

Approx. 5 months to complete

Suggested 9 hours/week
Available languages

English

Subtitles: English...

What you will learn

  • Check

    Compare ML for Finance with ML in Technology (image and speech recognition, robotics, etc.)

  • Check

    Describe linear regression and classification models and methods of their evaluation

  • Check

    Explain how Reinforcement Learning is used for stock trading

  • Check

    Become familiar with popular approaches to modeling market frictions and feedback effects for option trading.

Skills you will gain

Predictive ModellingFinancial EngineeringMachine LearningTensorflowReinforcement Learning
100% online courses

100% online courses

Start instantly and learn at your own schedule.
Flexible Schedule

Flexible Schedule

Set and maintain flexible deadlines.
Intermediate Level

Intermediate Level

Basic math including calculus and linear algebra, basic probability theory and statistics, and programming skills in Python.

Hours to complete

Approx. 5 months to complete

Suggested 9 hours/week
Available languages

English

Subtitles: English...

How the Specialization Works

Take Courses

A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.

Hands-on Project

Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.

Earn a Certificate

When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.

how it works

There are 4 Courses in this Specialization

Course1

Guided Tour of Machine Learning in Finance

3.6
199 ratings
82 reviews
This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to. The course is designed for three categories of students: Practitioners working at financial institutions such as banks, asset management firms or hedge funds Individuals interested in applications of ML for personal day trading Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course....
Course2

Fundamentals of Machine Learning in Finance

3.5
83 ratings
19 reviews
The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance. A learner with some or no previous knowledge of Machine Learning (ML) will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance. Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy. The course is designed for three categories of students: Practitioners working at financial institutions such as banks, asset management firms or hedge funds Individuals interested in applications of ML for personal day trading Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course....
Course3

Reinforcement Learning in Finance

3.4
27 ratings
9 reviews
This course aims at introducing the fundamental concepts of Reinforcement Learning (RL), and develop use cases for applications of RL for option valuation, trading, and asset management. Prerequisites are the courses "Guided Tour of Machine Learning in Finance" and "Fundamentals of Machine Learning in Finance". Students are expected to know the lognormal process and how it can be simulated. Knowledge of option pricing is not assumed but desirable....
Course4

Overview of Advanced Methods of Reinforcement Learning in Finance

3.6
16 ratings
2 reviews
In the last course of our specialization, Overview of Advanced Methods of Reinforcement Learning in Finance, we will take a deeper look into topics discussed in our third course, Reinforcement Learning in Finance. In particular, we will talk about links between Reinforcement Learning, option pricing and physics, implications of Inverse Reinforcement Learning for modeling market impact and price dynamics, and perception-action cycles in Reinforcement Learning. Finally, we will overview trending and potential applications of Reinforcement Learning for high frequency trading, cryptocurrencies, peer-to-peer lending, and more....

Instructor

About New York University Tandon School of Engineering

Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship....

Frequently Asked Questions

  • Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

  • This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

  • This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

  • Prerequisites for the specialization are basic math including calculus and linear algebra, basic probability theory and statistics, and some programming skills in Python. For students that are not familiar with Python and IPython / Jupyter notebooks, reference to tutorials are provided as a part of further reading.

More questions? Visit the Learner Help Center.