I am deeply passionate about Natural Language Processing (NLP), Machine Learning (ML), Deep Learning (DL), and Data Science and Analytics. In NLP, my focus is on leveraging transformer models like BERT and GPT for text classification, named entity recognition (NER), and question answering systems. My ML interests include both supervised learning (e.g., regression, classification) and unsupervised learning (e.g., clustering, dimensionality reduction), with a keen interest in model evaluation and feature engineering. In DL, I work with neural networks, including CNNs and RNNs, sequence-to-sequence models, transfer learning, and large language models. My data science expertise encompasses data wrangling, exploratory data analysis (EDA), statistical analysis, and predictive modeling, utilizing tools such as Hugging Face Transformers, TensorFlow, PyTorch, scikit-learn, and data visualization libraries like matplotlib and seaborn.