• 1.2 Hours

  • 4.5/5

  • Beginner

What you'll Learn

  • Explore the evolution of NLP.

  • Learn Large Language Model techniques and build real-world NLP applications.

  • Hands-On Experience: Engage with exercises designed to reinforce your learning and apply concepts in real-world scenarios.

Course curriculum

  • 1
    Introduction
    • Course Objective
    • Course Handouts
    • The Exponential Growth
  • 2
    The Evolution of NLP
    • The Evolution of NLP: Symbolic NLP
    • The Evolution of NLP: Statistical NLP
    • The Evolution of NLP: Deep Learning
    • The Evolution of NLP: Deep Learning Era II
    • The Evolution of NLP: Tranformers and Evolution
    • Quiz
  • 3
    What are Large Language Models?
    • Introduction to Large Language Model
    • What is a Large Language Model?
    • Understanding Foundational Models
    • Different types of LLMs: Based on Response
    • Different types of LLMs: Based on Model Architecture
    • Quiz
  • 4
    The Current State of the Art in LLMs
    • The Current State of the Art in LLMs
  • 5
    Generative AI - Glossary
    • Generative AI- Glossary
  • 6
    Your Feedback Matters!
    • Your Feedback Matters!

Instructor

Kunal Jain, Founder & CEO, Analytics Vidhya

Kunal has 15+ years of experience in the field of Data Science and is the founder and CEO of Analytics Vidhya- the world's 2nd largest Data Science community.
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Instructor

Who Should Enroll?

  • All looking to expand their skill set and leverage LLMs across different industries.

  • For those setting out on their journey to master language data analysis and leave a mark in the tech world.

FAQ

  • What are Large Language Models (LLMs), and why are they important?

    LLMs are AI models trained on vast amounts of text to understand and generate human-like language. They power applications like chatbots, content creation, and code generation.

  • How has NLP evolved over time?

    NLP has progressed through several phases: Symbolic NLP (rule-based methods), Statistical NLP (probabilistic models), Deep Learning NLP (neural networks), and now Transformers and Large Language Models (LLMs), which provide state-of-the-art performance.

  • What role do transformers play in the advancement of NLP?

    Transformers introduced the self-attention mechanism, allowing models to analyze entire text sequences at once rather than processing them sequentially. This led to faster, more accurate, and context-aware language models like GPT, BERT, and T5.

  • Will I receive a certificate upon completing the course?

    Yes, the course provides a certification upon completion.

  • What challenges do LLMs face in real-world applications?

    Hallucination: Generating incorrect or misleading information, Bias: Learning biases from training data, Compute Costs: High energy and hardware requirements, Interpretability: Difficulty in understanding why a model makes certain decisions.