Course Learning Outcomes

By the end of this course, I developed a strong understanding of the foundational concepts in Natural Language Processing (NLP), including the theoretical principles and practical techniques behind modern language models. I gained hands-on experience with various NLP tasks such as text classification, named entity recognition, sentiment analysis, machine translation, and question answering. A major part of the course focused on exploring deep learning architectures like transformers, which form the backbone of state-of-the-art models including BERT, BART, T5, and Whisper. I also studied generative approaches such as autoencoders and Generative Adversarial Networks (GANs), understanding how they contribute to text generation, reconstruction, and data augmentation. Through a combination of lectures, assignments, and project work, I learned how to build end-to-end NLP systems capable of handling complex tasks like transcription, abstractive summarization, and quiz generation. Additionally, I developed the ability to critically analyze research literature, identify gaps, and propose innovative solutions. The course also enhanced my skills in team collaboration, technical writing, and presenting our work in the form of a research paper and poster, preparing me for future academic and industry contributions in the field of AI and NLP.