This programme empowers you with the skills and confidence to exploit opportunities and accelerate data-driven investing decisions. Through lectures, real-world case studies, discussions, and hands-on exercises you will gain insights into various real-world applications.
DurationThis programme empowers you with the skills and confidence to exploit opportunities and accelerate data-driven investing decisions. Through lectures, real-world case studies, discussions, and hands-on exercises you will gain insights into various real-world applications of alternative data and machine learning in investing decisions.
Learning Objectives:
Core Focus:
The Asian Institute of Digital Finance (AIDF) is a university-level institute in the National University of Singapore (NUS), jointly founded by The Monetary Authority of Singapore (MAS), the National Research Foundation (NRF) and NUS.
AIDF aspires to be a thought leader, a FinTech knowledge hub, and an experimental site for developing digital financial technologies as well as for nurturing current and future FinTech researchers and practitioners in Asia.
The 55375AC: Fundamentals of Machine Learning certification provides validation for a comprehensive understanding of machine learning principles. It includes key areas such as algorithms, data models, statistics, data analysis, and prediction systems.
This course covers the fundamentals of machine learning principles and practices, with a practical focus. You will be introduced to the concept of Supervised and Unsupervised machine learning with the help of Python libraries such as scikit-learn and pandas.
This one-day instructor-led course introduces participants to the big data capabilities of Google Cloud Platform and showcases the ease, flexibility, and power of big data solutions on Google Cloud Platform.
This 3-day instructor-led course provides you with the knowledge to understand the fundamentals of Data Science and Machine Learning, creating simple machine learning implementation in Cloud.
In this advanced course, participants will learn specific concepts and advanced techniques of machine learning, such as Support Vector Machines, Random Forest, Boosting and Stacking, Cross Validation, Pipeline and many more.
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