Just Enough Python for Data Science

Understand the core Python constructs needed to build scalable data science and machine learning applications

Machine Learning and AI for Financial Professionals

Learn how to build pragmatic AI and ML applications with case studies in finance

Model Risk Management For Machine Learning Models

Address key model risk management and validation challenges when deploying data science and machine learning models in the enterprise

The FinTech Bootcamp: The 8 Facets of FinTech

Learn about the drivers and opportunities in Fintech as we move towards digital automation and online services.

Algorithmic Auditing

Learn about the how Algorithmic auditing best practices can be adopted within an organization.

The Risk in ML Models: Stress, Scenario Testing & Evaluation

Learn how to evaluate risk in machine models in this 6-week program geared towards practitioners.

MATLAB for Finance

Learn how to build financial applications with MATLAB and various toolboxes.

Anomaly Detection Workshop

Learn how to detect anomalies and outliers in cross-sectional and time series datasets using Data Science and ML techniques

Natural Language Processing Workshop

Understand key innovations in NLP and Neural Networks and how to build production-quality NLP applications

Deep Learning in Finance Workshop

Master deep-learning techniques and learn how to structure problems, design, build and test ML applications with Tensorflow, Keras and Pytorch

Stress testing in the age of Covid-19

Coming Soon

Synthetic data generation

Coming Soon

Privacy and Machine Learning in Finance

Coming Soon

Machine Learning for Asset Managers

Coming Soon

Responsible AI in Action Workshop

Coming Soon

Explainable AI in Finance Workshop

Coming Soon

Fundamentals of Deep Learning

Explore the fundamentals of deep learning by training neural networks and using results to improve performance and capabilities.

Fundamentals of Accelerated Data Science with RAPIDS

Learn how to perform multiple analysis tasks on large datasets using RAPIDS, a collection of data science libraries that allows end-to-end GPU acceleration

Building Transformer-Based Natural Language Processing Applications

Learn how to use Transformer-based natural language processing models for text classification tasks, such as categorizing documents.

Engineering Machine Learning Systems

In this course, we will discuss key aspects of the machine learning life cycle and how to move machine learning models into production.