Upon successful completion of this course, students will be able to:
• Apply advanced data engineering technologies using APIs and relational databases and perform ETL operations on industry-related data.
• Use advanced machine learning technologies like Deep Learning (DL).
• Develop models using frameworks for ML and DL.
• Understand and apply Time Series Analysis and forecasting techniques.
• Develop and deploy Web Applications using web development frameworks.
• Do hands-on project work and implementations in various FinTech verticals.

Machine Learning and Forecasting:
Intuition and the Model Building Process
Optimization (Convexity, Grad Descent)
Supervised ML (Regression, Classification Algorithms, and Models)
Unsupervised Learning (Clustering, PCA, and other Algorithms)
Time Series Analysis

Programming for Data Science – Development Frameworks:
Machine Learning Frameworks – Scikit Learn, Pytorch
Machine Learning Deployment – Web Apps

Advanced-Data Engineering:
Data Wrangling – ETL, Mapping, Scraping, Formatting

Advanced Machine Learning:
Anomaly Detection (Gaussian Approach, DBSCAN)
Deep Learning (CNN, NLP, RNN, Frameworks)
Time Series Forecasting – ARIMA and RNN Methods

Clinical Work:
Sandbox Projects – Predictive Analytics for Industry Applications
Programming – Natural Language Processing (NLP)