About the Course

In this workshop, students will be introduced to financial services, trading and the importance of AI/ML in the fintech industry with a set of case studies. Students will learn fundamental concepts of AI/ML, including supervised/unsupervised learning, bias-variance tradeoff, principal component analysis and neural networks. You will get hands-on experience in obtaining financial data via Quandl, or Yahoo Finance and understanding financial data and structure the data in a way that is amenable to ML algorithms. Students will be equipped with skill to implement machine learning algorithms to extract key features from financial datasets. Students will also be trained to develop fintech web applications using modern web application frameworks reactJS, python-flask and basic DB.

Learning Outcomes:

• Understand and appreciate the growing importance of AI/ML in the Financial Industry.
• Understand the and distinguish between supervised machine learning (ML), unsupervised ML, deep learning and artificial intelligence.
• Understanding statistical and mathematical models and their limitations.
• Understand Financial datasets and prepare the data for ML using Python libraries.
• Build and apply appropriate AI/ML models and data processing techniques using Python libraries for business decisions in financial settings.
• Use reactJS, python-flask, basic DB operations (CURD) to build fintech web applications.

Prerequisites: Python Programming Knowledge (Basic data structures and numpy):