🔥 Complete Embedding Models Tutorial for Beginners | Explained in Tamil | NLP & GenAI | RAG | Agents

Embedding Models — Explained in Tamil | GenAI | Agents | RAG Level: Beginner to Advanced All Complete Tutorials for Beginners: RAG: https://www.youtube.com/watch?v=4Qp5D5hcE4A CrewAI Agents: https://www.youtube.com/watch?v=PgPo9WHQczw LangGraph Agents: https://www.youtube.com/watch?v=vVtzWXTv3vM MCP: https://www.youtube.com/watch?v=2wyaDf04n_I FastAPI: https://www.youtube.com/watch?v=DRPpaFNpS-8 Fine-Tuning: https://www.youtube.com/watch?v=gOOS3k-7t6U Socials: 1:1 Mentorship : https://topmate.io/akash_balakrishnan/706031 LinkedIn: https://linkedin.com/in/akashb22 Instagram: https://instagram.com/ai.with.akash 📖 About This Video: This is a comprehensive Tamil-language series on Embedding Models, taking learners on a structured journey from the very basics of NLP all the way to hands-on BERT training and fine-tuning — making complex AI concepts accessible to Tamil-speaking audiences. 📚 Topics Covered Embedding Model Introduction — A beginner-friendly introduction to what embedding models are, why they matter, and how they power modern AI systems like GenAI, RAG pipelines, and AI Agents. Types of Embedding Models (2013–2026) — A historical walkthrough of how embedding models have evolved over the years, from early techniques to the latest approaches in 2026. Vector Embeddings — Explains how words and sentences are represented as numerical vectors, forming the backbone of all embedding-based systems. Word2Vec — CBoW & Skip-gram — A deep dive into Word2Vec, covering both the Continuous Bag of Words and Skip-gram approaches to learning word representations. RNN and LSTM Overview — Covers Recurrent Neural Networks and Long Short-Term Memory networks, providing the sequential modeling context needed before understanding Transformers. Transformer Overview — Explains the Transformer architecture, the foundation of modern language models like BERT, in a clear and visual way. Tokenizers in Detail — A thorough look at how tokenizers work, breaking down text into tok