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Module 1: Introduction to Generative AI

Understanding the Concept of Generative AI and its Significance

•   Understand the fundamentals of generative AI and its applications.
•   Compare generative AI with discriminative models.
•   Explore recent advancements and research in generative AI.
•   Learn the end-to-end project lifecycle for generative AI.

Module 2: Text Preprocessing and Word Embedding

Introduction to Programming and Tools

•   Implement basic text preprocessing techniques: tokenization, normalization, stemming, and lemmatization.
•   Understand and apply word embeddings: Word2Vec, GloVe, and FastText.

Module 3: Deep Dive into Generative Models

Introduction to Deep Learning

•   Introduction to deep learning fundamentals.
•   Learn about Generative Adversarial Networks (GANs).
•   Understand Variational Autoencoders (VAEs).
•   Dive into Transformer models and their applications.

Module 4: Advanced Applications, Ethics, and Deployment

Advanced NLP, Large Language Models (LLMs), and Ethics

•   Explore advanced NLP techniques and Large Language Models (LLMs).
•   Understand the ethical considerations in deploying generative AI.
•   Learn about deployment and hardware considerations for generative AI systems.
•   Introduction to PyTorch for deep learning applications.
•   Fine-tune Large Language Models (LLMs) for specific tasks.

Module 5: Generative AI with LLMs and LLM-Powered Applications

Building Generative Models with Large Language Models

•   Learn how to build generative models using LLMs.
•   Study case studies of LLM-powered applications.
•   Understand the integration of LLMs into existing systems.
•   Explore OpenAI’s ready-to-use models and their applications.

Module 6: Guide to OpenAI and its Ready-to-Use Models with Applications

Overview of OpenAI and its Offerings

•   Understand the range of models offered by OpenAI.
•   Learn how to use pre-built models from OpenAI.
•   Develop applications using OpenAI’s models.
•   Master prompt engineering with OpenAI models.

Module 7: Prompt Engineering Mastery with OpenAI

Basics of Prompt Engineering

•   Understand the fundamentals of prompt engineering.
•   Learn to craft effective prompts for desired outputs.
•   Master advanced prompt techniques.
•   Work through practical examples and case studies.

Module 8: Vector Database with Python for LLM Use Cases

Introduction to Vector Databases

•   Learn the basics of vector databases and their applications.
•   Set up a vector database with Python.
•   Explore use cases for vector databases in large language model applications.
•   Optimize performance and scale vector databases for efficient use.

Module 9: Hands-on with LangChain

Introduction to LangChain

•   Learn how to build and deploy applications with LangChain.
•   Participate in practical exercises and projects using LangChain.

Module 10: Hands-on with LangChain (Continued)

Advanced Features of LangChain

•   Explore advanced features of LangChain for enhanced application development.
•   Integrate LangChain with other tools and libraries.
•   Learn best practices and optimization techniques for LangChain-based projects.

Module 11: Practical Guide to LlamaIndex with LLMs

Introduction to LlamaIndex

•   Understand the purpose and functionality of LlamaIndex.
•   Build LLM applications using LlamaIndex.
•   Study case studies and real-world applications of LlamaIndex.

Module 12: End-to-End Projects

Project Planning and Design

•   Learn how to plan and design generative AI projects.
•   Implement and test your projects.
•   Deploy and maintain generative AI models.
•   Showcase and discuss project examples.

Module 13: Bonus: Additional Productive Tools to Explore

Overview of Additional Tools and Libraries

•   Discover additional tools and libraries for generative AI.
•   Integrate new tools into your workflow for improved productivity.
•   Enhance your workflow with advanced tools in generative AI development.