Natural Language Processing (NLP) with Transformers introduces students to modern NLP techniques with a focus on transformer-based models like BERT, GPT, and RoBERTa that have revolutionized the field. The course begins with foundational NLP tasks such as tokenization, stemming, lemmatization, part-of-speech tagging, named entity recognition, and syntactic parsing using libraries like NLTK and spaCy. Learners then transition to vectorization techniques including Bag of Words, TF-IDF, and word embeddings like Word2Vec and GloVe. From there, the focus shifts to the architecture and mechanics of transformers—explaining self-attention, positional encoding, multi-head attention, and encoder-decoder models. Students are introduced to the Hugging Face Transformers library, which simplifies fine-tuning large pre-trained models for a wide range of downstream tasks such as text classification, question answering, summarization, and translation. Hands-on projects include building sentiment analysis engines, chatbot interfaces, and semantic search systems. Emphasis is placed on training efficiency, model interpretability, and handling large datasets using techniques like token truncation, batching, and mixed-precision training. Students also explore deployment using ONNX and FastAPI. Ethical considerations such as bias in language models, adversarial text, and misinformation generation are addressed. By the end, learners will be equipped to build powerful NLP applications using state-of-the-art transformer architectures and fine-tuned models. This course is ideal for data scientists, NLP engineers, and AI practitioners working on real-world language processing solutions.