Semantic Search using BERT Embeddings | Modern Alternative to TF-IDF | NLP Project with Explanation

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Project Title: Semantic Search using BERT Embeddings — A Modern Alternative to TF-IDF for Information Retrieval In this video, I’ll explain how traditional keyword-based search models like TF-IDF fail to capture semantic meaning, and how BERT (Bidirectional Encoder Representations from Transformers) revolutionizes information retrieval by understanding context and intent. We’ll walk through the problem statement, algorithm, step-by-step explanation, and visual comparison between TF-IDF and BERT. By the end of this video, you’ll understand how to build a semantic search engine that retrieves documents based on meaning — not just words. 🧠 What You’ll Learn Difference between TF-IDF and Semantic Search How BERT embeddings capture contextual meaning Step-by-step working of the BERT algorithm Implementation details using Python and Jupyter Notebook Visualization of query similarity scores (TF-IDF vs BERT) Real-world applications of semantic search 🧩 Technologies Used Python 🐍 Hugging Face Transformers Scikit-Learn NumPy & Pandas Matplotlib Jupyter Notebook 📂 Project Resources 🧾 GitHub Repository: https://github.com/Dinesh-7021 ✍️ Blog Article (Medium): Semantic Search using BERT Embeddings 🏁 Key Takeaways Semantic Search bridges the gap between literal and contextual retrieval. Using BERT embeddings, we achieve: Higher accuracy Better relevance True AI-driven understanding of language 🔔 Don’t Forget To 👍 Like the video 💬 Comment your thoughts or questions 📢 Share with friends interested in NLP or AI projects 🔔 Subscribe for more AI, ML, and NLP project videos