An Introduction to Machine Learning in Quantitative Finance
UCL (University College London)
  • Start Date: 23 Jan, 2023
  • 4 weeks
  • Study Content: Videos

Discover how machine learning can be used to solve financial data problems and create informative insights and predictions.

Course Fee: Free
Certificate Cost: See Fees and Eligibility

Course Description

This course is made available through the eLearnAfrica and FutureLearn partnership.

Explore the applications of machine learning for quantitative finance

Over the past few years, machine learning (ML) has gradually permeated the financial sector, reshaping the landscape of quantitative finance as we know it.

This four-week course from University College London will demystify machine learning by uncovering its underlying mathematics and showing how to apply ML methods to real-world financial data.

Learn to use supervised learning models such as linear regression

Supervised learning is a category of machine learning that uses algorithms to classify data and create predictions.

You’ll be provided with an overview of supervised learning, as well as linear and non-linear regression with regularisation and classification. This will enable you to learn other new supervised learning algorithms in a systematic manner.

Understand how to use deep learning for predictive analytics in finance

Huge datasets are incredibly common in the financial sector, and present a significant challenge to researchers and analysts.

On this course, you’ll familiarise yourself with neural networks and understand how deep learning can be used to analyse large datasets and create accurate financial predictions. At the end of the course, you’ll put your learning into practice by tackling an empirical financial data problem using machine learning end-to-end.

Study with the experts at University College London

Your course educators are faculty members of the financial mathematics group at the UCL and Shanghai University.

With the help of their extensive research and experience, you’ll be empowered to solve real-world financial challenges through the application of modern machine learning methods.

This course is designed for anyone interested in machine learning and quantitative finance with a basic background in probability and Python programming.

It will be of particular interest to final-year undergraduate students or MSc students in financial mathematics or related subjects, pursuing a career in quantitative finance or data science.

It will also be suited to practitioners in quantitative finance.


Certificate cost may vary. You will be redirected to the host page for cost and payment options.

UCL (University College London)

UCL was founded in 1826. It was the first English university established after Oxford and Cambridge, the first to open up university education to those previously excluded from it, and the first to provide systematic teaching of law, architecture and medicine.

UCL is among the world’s top universities, as reflected by performance in a range of international rankings and tables. UCL currently has over 35,000 students from 150 countries and over 11,000 staff. UCL’s annual income is more than £1 billion.


This institution is available on eLearnAfrica through partnership with FutureLearn.

You may be able to download course materials after enrolling in this course. If not, all of the necessary course materials provided by the course instructor will be available on the provider's course page.

By enrolling in a course on or through eLearnAfrica, you are joining a special worldwide community of learners. The aspiration of eLearnAfrica is to provide anyone with an internet connection access to courses from the best universities and institutions in the world and to provide our learners the best educational experience internet technology enables. You are a part of the community that will help eLearnAfrica achieve this goal. eLearnAfrica depends upon your motivation to learn the material and to do so with honesty and academic integrity. In order to participate in eLearnAfrica, you must agree to the Honor Code below and any additional terms specific to a class.


By enrolling in a course, program, or degree hosted on the eLearnAfrica App or Site, I agree that I will:

  • Complete all tests and assignments on my own, unless collaboration on an assignment is explicitly permitted.

  • Maintain only one user account and not let anyone else use my username and/or password.

  • Not engage in any activity that would dishonestly improve my results, or improve or hurt the results of others.

  • Not post answers to problems that are being used to assess student performance.

  • Abide by any and all requirements of the eLearnAfrica Participants as may regard the expectations of civil or academic behavior or of community standards.


If you are found in violation of the Terms and Conditions or Honor Code, you may be subject to one or more of the following actions:

  • Receiving a zero or no credit for an assignment;

  • Having any certificate earned in the course withheld or revoked;

  • Being unenrolled from a course, learning program or degree program; or

  • Termination of your use of the App and/or Site.

  • Additional actions may be taken at the sole discretion of eLearnAfrica and eLearnAfrica course providers. 

No refunds will be issued in the case of any corrective action for such violations.

Honor code violations will be determined at the sole discretion of eLearnAfrica, the Partners, or Members. You will be notified if a determination has been made that you have violated this honor code and you will be informed of the corresponding action to be taken as a result of the violation.


Please note that we review and may make changes to this Honor Code from time to time. Any changes to this Honor Code will be effective immediately upon posting on this page, with an updated effective date. By accessing the App and/or Site after any changes have been made, you signify your agreement on a prospective basis to the modified Honor Code and any changes contained therein. Be sure to return to this page periodically to ensure familiarity with the most current version of this Honor Code.

Effective Date: September 22, 2016