Understand the core Python constructs needed to build scalable data science and machine learning
Learn how to build pragmatic AI and ML applications with case studies in finance
Address the key model risk management and validation challenges when deploying data science and
machine learning models in the enterprise
Learn about the drivers and opportunities in Fintech as we move towards digital automation and
Learn how to build financial applications with MATLAB and various toolboxes.
Learn how to detect anomalies and outliers in cross-sectional and time series datasets using Data
Science and ML techniques
Understand key innovations in NLP and Neural Networks and how to build production-quality NLP
Master deep-learning techniques and learn how to structure problems, design, build and test ML
applications with Tensorflow, Keras and Pytorch
Explore the fundamentals of deep learning by training neural networks and using results to improve
performance and capabilities.
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 for data science workflows
In this workshop, you’ll learn how to use Transformer-based natural language processing models for
text classification tasks, such as categorizing documents.
In this course, we will discuss key aspects of the machine learning life cycle and how to move machine learning models into production.
This course is delivered by QuantUniversity. Participants will have access to materials
put together by NVIDIA's Deep Learning
Institute and will receive a certificate by NVIDIA.
This course is prepared and delivered by QuantUniversity