Evaluate the performance of several state-of-the-art deep learning techniques on various text classification datasets. This project is part of Master's Thesis in Computer Control and Automation, NTU.

Counting rice grain and detecting the broken rice grains in the image. Solving the Touching grain problems using WaterShed algorithm.

Build a serverless deep learning model (end-to-end implementation) to classify real-world clothes images using TensorFlow, TensorFlow Lite, Docker, and AWS Lambda with API Gateway.

Personal Data Science Projects for 6 Business Cases in Sales, Marketing, Operations, Human Resource, Public Relations, and Production/Maintenance Departments (in progress).

The IBM Applied Data Science Capstone: The Battle of the Neighborhoods. The project is to cluster Toronto neighborhoods using KMeans to find the best location for starting a coffee shop business.

Documentation of personal programming projects using Python.

Personal data analytics project using real-world datasets in Python