Description
📚 Modules & Learning Objectives
Module 1: Introduction to NLP
- What is NLP?
- Key milestones and applications
- NLP pipeline: Tokenization, Lemmatization, POS tagging, etc.
- Use cases in industry (chatbots, sentiment analysis, summarization)
Module 2: Evolution of Language Models
- Rule-based systems to machine learning
- Traditional models: n-gram, TF-IDF, Word2Vec, GloVe
- Introduction to sequence models: RNNs, LSTMs
Module 3: Rise of LLMs
- What are LLMs?
- Architecture of Transformers (BERT, GPT, T5, etc.)
- Pre-training vs Fine-tuning
- Hugging Face and OpenAI Ecosystems
Module 4: Building with LLMs
Prompt engineering basics
Fine-tuning vs Retrieval-Augmented Generation (RAG)
Ethical considerations (bias, hallucination, data privacy)
Hands-on demo (optional): Using GPT or open-source LLMs via API
Module 5: Real-World Applications & Careers
- Enterprise use cases (customer support, legal, healthcare, etc.)
- Building AI-powered apps with NLP
- Jobs and skills in the NLP/LLM domain
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🛠️ Tools & Technologies Covered
- Python NLP libraries: spaCy, NLTK, Transformers (Hugging Face)
- APIs: OpenAI, Cohere, LangChain
- Platforms: Google Colab, Jupyter, Streamlit
🧠 Who Should Attend
- Developers and Data Scientists
- AI/ML enthusiasts
- Entrepreneurs building AI products
- Students and educators in Computer Science
🎯 Key Takeaways
- Understand how LLMs and NLP work under the hood
- Learn to build and integrate LLMs in real-world apps
- Gain hands-on experience with modern NLP tools
- Explore career paths in AI & NLP
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