Generative AI and Prompt Engineering Certification Course

• Introduction to Prompt Engineering
• Foundations of Prompting.
• Use case: Practical exercises and demonstrations on creating prompts for various tasks using ChatGPT.
  • Information retrieval
  • Creative writing
• Explore different types of Prompts, Prompting Strategies, and Principles of effective prompt designing
• Prompting Techniques:
  • Zero-shot and One-shot Prompting
  • Information Retrieval and Creative Writing
  • Advanced Prompt Structuring
  • Prompt Reframing and Combination
  • Interactive Storytelling and Language Translation
  • Sequential Prompting and Self-Consistency
  • Analogical and Meta-Ask Prompting
  • ReAct and OPRO
  • Chain Of Thought
  • Iterative Prompting
• Best practices for maintaining user privacy and security measures to prevent misuse of AI technology.
• Exploring Prompting Tools: Introduction to libraries, models, and frameworks available for prompt engineering.

• AI vs XAI vs GenAI
• Intro to Generative AI for Text Data
• Deriving insights by performing sentiment analysis with Text Mining and NLP
  • Text Mining & NLP
    • NLU vs NLG
    • Data Preprocessing
      • Tokenization
      • Bag of Words
      • DTM/TDM and TFIDF
      • Word Cloud – Uni-Gram, Bi-Gram
      • Text Summarization
      • Language Models
    • History of NN and RNN
    • Challenges with Traditional RNNs
    • LSTM
    • GRU
    • GANs
    • Autoencoders
    • Diffusion Models
• Learn the Transformer architectures implemented for machine translation and automated summarization.
  • Transformers
    • Encoders
    • Decoders
    • Transformer Architecture
• Learn about the groundbreaking uses of BERT and GPT in automated content creation.
  • Seq2Seq & Attention Mechanism
  • BERT & GPT Models
• Implementing Chatbots to enhance interaction quality and operational efficiency for effective customer engagement across industries.
  • Chatbot History
  • Hands-on – Traditional Chatbot development

• Learn how LLMs like GPT and BERT are revolutionizing with improved accuracy of information retrieval across digital libraries.
  • LLMs 101
  • Semantic Search
  • Types of Large Language Models
  • Datasets for LLMs
  • Model Evaluation & Benchmarks
  • Fine Tuning Techniques
  • Hyper Parameters of LLMs
  • Regularization Techniques of LLMs
  • Customization – Embeddings and Models
  • Challenges
  • Hallucination Detection Techniques
  • Foundation Models
  • Small Language Models
  • Frameworks for Building LLM Applications
  • RAG (Retrieval Augmented Generation)
• Learn to build specialized models in healthcare, and the financial sector as part of Domain-Specific LLMs.

• Exploring the World of AI-generated imagery with Dall-E 3 and Imagen.
• Learn the creation of realistic video sequences from textual inputs, significantly reducing production time and costs.
• Leveraging tools like ViT and SDXL Turbo in Automated Image Analysis.
• Explore the use of advanced AI models like BLIP-2 and Flamingo in enhancing user experience.
• Discover how models are used in various business platforms for image-based applications to enhance customer experience and engagement

• Learn Generative AI tools like Soundful and Lyria to transform music production.
• Enhancing Multimedia Experiences with the Video-Audio-Text Transformer (VATT).
• Improving audio quality and enabling innovative sound manipulation techniques with models like Distil-Whisper and SALMONN.
• Enhancing user engagement through tailored listening experiences.

• Platforms that respond to both text and image inputs.
• Integrate visual and textual information with like LLaVA and GPT-4V
• Transforming digital marketing, storytelling, and educational content generation with models like Fuyu and Fuyu-8B that combine text, image, and video.
• Learn about the importance of diverse datasets like PMD, COCO, and Flickr30K in training AI.
• Benchmarks: VQAv2 and TextVQA - to evaluate the effectiveness of multimodal AI models.
• Visual search and image-based analysis with innovative models BriVL and Qwen-VL.
• Creating enriched multimedia experiences in virtual reality, video editing, and automated captioning with SALMONN.

• Generative AI on AWS:
  • Develop Generative AI models with AWS services: Amazon Bedrock and SageMaker.
  • Generating code snippets and entire functions with Amazon CodeWhisperer.
  • Using high-performance computing services AWS Trainium and Inferentia to Optimize the training and deployment of AI models
• Broad spectrum of Generative AI applications on Cloud platforms – Azure
  • Azure OpenAI Service
  • Azure AI studio
  • Microsoft Copilot
• Broad spectrum of Generative AI applications on Cloud platforms – Azure
  • Gemini
  • Vertex AI
  • Duet AI in GCP

Capstone:

Use case1: Developing a customer service Chatbot using LLMs tailored for local needs.

Use case2: AI-assisted document translation and topic modelling to identify key subjects and trends.

Use Case3: Chatbot for web-scraping data

Use Case4: Pharmaceutical Classification System using Large Language Models

Use Case5: PharmaBot: A Natural Response Chatbot for Drug Classification and Information (LLM)