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Generative AI NLP Specialization | Level 1

Building Intelligent NLP Solutions with Generative AI


3 Days


Intermediate Level

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Welcome to the “Generative AI NLP Specialization Level 1” course, this course is designed to provide you with a basic understanding of the exciting field of Natural language processing using Generative AI, focusing on its various generative models and techniques. Over the span of this course, you will delve into the intricate world of generating text, and embeddings using cutting-edge models. With hands-on labs and real-world applications, you will not only grasp the theoretical foundations but also gain practical skills to implement generative models in your own projects.


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  • Developers and software engineers interested in learning NLP using GenerativeAI techniques 
  • AI enthusiasts and professionals aiming to build intelligent and innovative solutions
  • Even Data scientists and machine learning practitioners seeking to enhance their skills in working with GenAI models
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  • Understanding Fundamentals of Generative AI 
  • Types of Generative Models – autoregressive models, variational autoencoders, and generative adversarial networks (GANs)
  • GAN Architecture and tuning process
  • GAN variants
  • Generating synthetic data using GANs
  • Categorizing generative models based on learning algorithms: likelihood-based vs. likelihood-free
  • Motivation for generative modeling compared to discriminative models
  • Characteristics of generative models: density estimation, data simulation, representation learning
  • Techniques of generating text using generative models 
  • Text generation using RNNs & Transformers.
  • Industry Use Cases
  • Lab: RNN & Transformer based Text Generation
  • Generating embeddings
  • Embedding techniques
  • Sentence/Document embeddings
  • Lab: Embeddings
  • Introduction to Vector Database
  • Building a Vector database
  • Lab: Building a Vector DB
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Participants should have attended Foundation for Generative AI course or have an equivalent knowledge.


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