Project 1: How AI Reads Pharmaceutical Documents

I explored Machine Learning, Deep Learning, LLMs, and NLP to understand how AI interprets pharmaceutical documents. By analyzing a real pharmaceutical documentation file, I gained insights into how AI models handle both structured and unstructured data, laying a solid foundation for improving

Project 3: Data Extraction from Documents Using Python

I converted digital PDFs into machine-readable formats using Python, employing tools like PyMuPDF and pdfplumber. By applying field heuristics, I successfully extracted key data fields, which are crucial for Pfizer's compliance workflow, including document dates and types.

Project 5: Introduction to Retrieval-Augmented Generation (RAG)

AI models can process vast amounts of text, but finding the right information quickly is a challenge. In this project, you’ll learn how to build a Retrieval-Augmented Generation (RAG) pipeline using LlamaIndex to help AI retrieve relevant data from large document sets.

Project 2: Learn to Work with Python for AI-Powered Document Processing

I utilized Python for data processing and cleaning of pharmaceutical documents, preparing them for AI automation. Through hands-on experience with Google Colab, I learned to standardize information and enhance OCR accuracy through effective image processing techniques.

Project 4: Advanced OCR Comparison and Layout-Aware Extraction

I conducted a comparative analysis of three OCR engines—Tesseract, PaddleOCR, and EasyOCR—on scanned pharmaceutical supplier documentation. I evaluated their effectiveness in text extraction and formatting preservation, ultimately recommending the best tool for real-world automation.