Advanced Specialization: NLP & LLMs

Master the techniques to process, understand, and generate human language with state-of-the-art models.

• Duration: 10 Weeks (Part-time: ~15 hours/week)

• Prerequisites: "Machine Learning Engineer" course or strong proficiency in Python and ML.

• Learning Format: Advanced, research-oriented curriculum with weekly hands-on coding labs focused on transformer architectures.

• Syllabus & What You'll Learn:

  • Module 1: Fundamentals of Natural Language Processing (NLP)

    • Text Preprocessing, TF-IDF, Word Embeddings (Word2Vec, GloVe).

  • Module 2: Transformer Architecture & Attention Mechanism

    • In-depth study of the architecture behind models like GPT and BERT.

    • Implementing a transformer from scratch.

  • Module 3: Large Language Models (LLMs) in Practice

    • Prompt Engineering and Zero/One-Shot Learning techniques.

    • Fine-tuning pre-trained LLMs (e.g., Llama 2, Mistral) for specific tasks (sentiment analysis, text summarization).

    • Cost-effective fine-tuning with LoRA (Low-Rank Adaptation).

  • Module 4: Building LLM-Powered Applications

    • Creating RAG (Retrieval-Augmented Generation) systems for custom knowledge bases.

    • Introduction to LLM evaluation and monitoring.

  • Capstone Project: Build a custom chatbot for a specific domain (e.g., legal or medical QA) using RAG and a fine-tuned open-source LLM.

• Outcome: You will gain specialized skills to work with and deploy cutting-edge NLP and LLM solutions, positioning you for roles like NLP Engineer or AI Specialist.