Join us for an insightful and hands-on session that traces the fascinating journey of text representation in Natural Language Processing (NLP). From the simplicity of Bag-of-Words and TF-IDF to the intelligence of context-aware embeddings like Word2Vec and BERT, this workshop walks through how machines have learned to "understand" human language.

Led by Dr. Jaideepsinh Raulji, Assistant Professor at Navrachana University and an experienced NLP researcher, this session will focus on implementing encoding techniques using Python libraries like scikit-learn, NLTK, and Sentence-Transformers. Whether you're just stepping into NLP or refining your ML workflow, you’ll gain a clear, empirical understanding of which encoding techniques work best for different tasks.

🧠 Why attend?

• Get hands-on with real-world NLP workflows

• Learn the pros and cons of different embedding strategies

• Upgrade your ML models by choosing the right text representation

• Ask your questions directly to an academic expert in NLP

🎯 What to expect:

• Overview of BoW, TF-IDF, Word2Vec, and BERT

• Live implementation of encoding techniques

• Unified text classification project for comparison

• Interactive Q&A and discussion on best practices

👥 Who should attend?

• Data science & ML enthusiasts

• NLP beginners & intermediate practitioners

• Python developers curious about AI

• Students and researchers exploring AI applications in language