Fine-tuning and Adapting GenAI Models in English
What you'll learn
- Learn to use Google Colab for unleashing the power of Python's text analysis and deep learning ecosystem
- Introduction to the theory and implementation of LLMs and Generative AI
- Get acquainted with common Large Language Model (LLM) frameworks including LangChain
- Introduction to the theory and implementation of LLM Optimization
- Introduction to optimization techniques such as soft prompting
- Introduction to RAGs
Requirements
- Prior experience of using Jupyter notebooks
- Prior exposure to NLP and generative AI Concepts
- Prior exposure to LLMs
Description
Fine-tuning and Adapting GenAI Models in English
Unlock the potential of Generative AI with our in-depth course, "Fine-tuning and Adapting GenAI Models in English". Designed for AI professionals, data scientists, and developers, this course provides a comprehensive exploration of Generative AI concepts, Large Language Models (LLMs), and the practical skills needed to adapt these cutting-edge technologies for real-world applications. Whether you’re new to GenAI or looking to refine your expertise, this course equips you to customize and optimize models to meet diverse use cases effectively.
Course Overview:
This course offers an immersive dive into the principles and applications of Generative AI, with a focus on fine-tuning and adapting LLMs using leading frameworks like OpenAI, Hugging Face, and LangChain. You’ll explore the theoretical foundations of GenAI, learn advanced prompt engineering techniques, and discover how Retrieval-Augmented Generation (RAG) enhances AI capabilities. With hands-on assignments and expert guidance, you’ll master the tools and methodologies required to build powerful AI solutions, such as domain-specific chatbots, summarization systems, and question-answering applications.
Key Learning Outcomes:
Foundations of Generative AI and LLMs:
Build a strong understanding of Generative AI and the architecture of LLMs, laying the groundwork for advanced fine-tuning techniques.Frameworks for LLMs:
Gain practical experience with tools like OpenAI APIs, Hugging Face Transformers, and LangChain to customize and deploy LLMs effectively.Prompt Engineering for Customization:
Learn how to design and optimize prompts to guide LLMs toward delivering precise, contextually relevant outputs.Fine-tuning Principles and Applications:
Discover how to fine-tune pre-trained models to adapt them for specific domains, improving performance and accuracy.Retrieval-Augmented Generation (RAG):
Master the integration of external knowledge sources with LLMs to build robust and context-aware AI systems.Building Real-world Applications:
Apply your knowledge to create solutions for text summarization, question answering, and other real-world use cases using LLMs.
Why Enroll?
Led by an expert in Generative AI with a proven track record of delivering impactful courses, this program combines cutting-edge theory with hands-on practice. By the end of the course, you’ll have the confidence and skills to fine-tune and adapt LLMs for a wide range of applications, from creating conversational agents to deploying intelligent content generation systems.
Ready to Transform Your AI Skills?
Join us and master the art of fine-tuning and adapting Generative AI models. Enroll today to stay ahead in the rapidly evolving field of AI and unlock new opportunities to innovate and lead in the AI-driven future!
Who this course is for:
- Students with prior exposure to NLP analysis
- Those interested in using LLM frameworks for learning more about your texts
- Students and practitioners of Artificial Intelligence (AI)
Instructor
- 4.4 Instructor Rating
- 28,305 Reviews
- 145,463 Students
- 61 Courses
I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.
I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC).