AutomationEdge, India

During my Machine Learning Internship at Automation Edge, I focused significantly on research-driven initiatives. I was in charge of creating word embeddings and text representations from customer complaints in order to improve the performance of Natural Language Processing (NLP) models. To improve the comprehension of textual client complaints, I used Named Entity Recognition (NER) and Part-of-Speech (POS) tagging approaches. As a result of these efforts, the model effectively predicted intents for a dataset of bank-related customer complaints. This project used a tech stack that included Machine Learning, Deep Learning, NLP, Spacy, and Textacy, demonstrating my dedication to cutting-edge technology and new solutions in machine learning and NLP.