Financial Machine Learning

This course is designed to provide a comprehensive understanding of machine learning techniques applied to financial markets. It combines theoretical knowledge with practical applications to equip participants with the skills necessary to build, evaluate, and deploy machine learning models in the financial domain.

We shall introduce you to the exciting and rapidly evolving field of Financial Machine Learning (FML). You will learn how machine learning algorithms are used to solve real-world problems in finance, including:

- Trading: Predicting market movements, identifying trading opportunities, and automating trading strategies.
- Risk Management: Assessing and managing financial risks, detecting fraud, and building robust financial models.
- Investment Management: Building optimal portfolios, predicting asset prices, and making informed investment decisions.
- Credit Scoring: Assessing creditworthiness, predicting default rates, and optimizing loan decisions.

By the end of this course, you will be able to:

- Understand the basic concepts of machine learning and how they apply to finance.
- Implement various machine learning algorithms for financial tasks.
- Evaluate the performance of machine learning models.
- Apply machine learning to solve real-world financial problems.

This training has been designed by experienced Data Scientists who will help you to understand the WHYs and HOWs of Algorithm Engineering.

Trainers:
Experts from the field of Maths, BFSI, IT, Data Science and Management, each with over 25 years of International experience working in EU/US/Australia

This course aims to provide participants with a strong foundation in applying machine learning techniques to financial markets, preparing them to tackle real-world problems in financial domain.

Who this course is for:
- Bank Professionals
- Managers
- People seeking technical jobs in BFSI Domain
- Software Engineers
- Computer Science undergraduates
- Data Scientists
- Designers and Analysts
- Team Leaders