From Data to Dialogue: End-to-End Generative AI & LLM Course

About This Course

Generative AI and Large Language Models (LLMs) like ChatGPT have transformed the way we interact with technology. From text generation to automated customer service, the possibilities are vast. This course is designed to teach participants the core skills needed to build, train, and deploy Generative AI models and LLMs, providing hands-on experience in solving real-world problems such as text classification.

Over the course of three modules, you’ll gain practical skills, from data preprocessing and embeddings to model training and deployment. You’ll learn key concepts in data representation, vectorization, and AI pipeline creation, all while getting experience with popular tools and techniques used in industry today.

Aim

To provide participants with the practical skills and knowledge required to master Generative AI and Large Language Models (LLMs), covering everything from data preprocessing to end-to-end AI pipeline creation and text classification applications.

Course Objectives

By the end of this course, participants will:

• Have a comprehensive understanding of Generative AI and LLMs

• Learn how to preprocess data and build end-to-end AI pipelines

• Master concepts like data representation, vectorization, and model training

• Gain hands-on experience in text classification using Generative AI and LLMs

• Understand the architecture and training process behind models like ChatGPT

• Be equipped to apply AI techniques to solve real-world problems

Course Structure

Module 1: Introduction to Generative AI & Data Preprocessing

• Overview of Generative AI:

  • Introduction to Generative AI and its real-world applications

  • Explore how Generative AI is transforming various industries

• Understanding the Basics of Generative Models:

  • Key concepts in Generative Models and their use cases

• End-to-End Generative AI Pipeline:

  • A walkthrough of the full pipeline, from data collection to model output

• Data Preprocessing and Cleaning:

  • Best practices for preparing datasets for effective model training

• Data Representation & Vectorization:

  • Techniques for representing textual and structured data for model training

• Hands-On Session:

  • Practical session applying text classification using Generative AI models

Module 2: Introduction to Large Language Models (LLMs)

• Understanding LLMs:

  • A deep dive into Large Language Models (LLMs) and their architecture

  • Role of LLMs in natural language processing (NLP)

• Intuition Behind Transformers:

  • Detailed exploration of transformer models and their significance in LLMs

• Training of ChatGPT and Similar Models:

  • Understanding how models like ChatGPT are trained, fine-tuned, and deployed

• Hands-On Session:

  • Practical session applying LLMs for text classification tasks

Module 3: Advanced Techniques & Real-World Applications

• Advanced Data Representation for LLMs:

  • Deep dive into data representation and vectorization techniques specifically for LLMs

• Fine-Tuning LLMs for Specific Tasks:

  • Techniques to fine-tune LLMs for specialized applications

• Real-World Use Cases:

  • Case studies showing how Generative AI and LLMs are applied in sectors like healthcare, finance, and customer service

• Final Hands-On Session:

  • End-to-End Project: Build, train, and apply Generative AI and LLMs to a real-world text classification problem

• Group Discussion/Q&A:

  • Discussion of local challenges and solutions in applying Generative AI and LLMs

Who Should Enrol?

• AI/ML professionals, data scientists, and engineers looking for hands-on experience with Generative AI and LLMs

• Students and researchers in AI, machine learning, and data science

• Industry professionals in sectors like healthcare, finance, and technology interested in applying AI to real-world problems

• Tech enthusiasts with a basic understanding of machine learning and Python programming