Master LLM Optimization: Boost AI Performance & Efficiency (Udemy.com)
Unlock advanced techniques for fine-tuning, scaling, and optimizing LLMs to enhance AI capabilities
Created by: Minerva Singh
Last updated December 2024
What you will learn
- Learn to use Google Colab for unleashing the power of Python's text analysis and deep learning ecosystem
- Introduction to the basic concepts around LLMs and Generative AI
- Get acquainted with common Large Language Model (LLM) frameworks including LangChain
- Learning about using the Hugging Face hub for accessing different LLMs
- Introduction to the theory and implementation of LLM Optimization
How it compares
Against the other 371 rated Udemy courses in Data Analysis on CourseDuck:
- Rating4.3 out of 5higher than 21% of them
- Reviews131#318 of 372 by number of reviews
- Length3 hoursshorter than 65% of them
- Last updatedDecember 2024earlier than 59% of them
Course Description
Disclaimer- This course contains the use of artificial intelligence
Master LLM Optimization: Boost AI Performance & Efficiency
Unlock the power of Large Language Models (LLMs) with our cutting-edge course, "Master LLM Optimization: Boost AI Performance & Efficiency." Designed for AI enthusiasts, data scientists, and developers, this course offers an in-depth journey into LLMs, focusing on optimization techniques that elevate AI capabilities. Whether you're a beginner in LLM implementation or an experienced practitioner seeking to refine your skills, this course equips you with the knowledge and tools to excel in this rapidly evolving field.
Course Overview:
This course deep dives into LLM frameworks like OpenAI, LangChain, and LLAMA-Index, empowering you to build and fine-tune AI solutions like Document-Reading Virtual Assistants. With a comprehensive curriculum, you'll explore the theory and practical implementation of LLM optimization, gaining hands-on experience with popular LLM models like GPT and Mistral through Hugging Face. By the end of the course, you’ll have mastered advanced techniques for harnessing LLMs, enabling you to develop AI systems that are both efficient and powerful.
Key Learning Outcomes:
Foundations of Generative AI and LLMs: Understand the core concepts of Gen AI and LLMs, laying a solid foundation for more advanced topics.
Introduction to LLM Frameworks: Get hands-on experience with popular LLM frameworks, including OpenAI, LangChain, and LLAMA-Index, enabling you to build and deploy AI applications with ease.
Accessing LLM Models: Learn how to access LLM models via Hugging Face, work with cutting-edge models like Mistral, and implement them effectively.
LLM Optimization Techniques: Discover advanced optimization methods such as quantization, fine-tuning, and scaling, essential for enhancing LLM performance in real-world applications.
Retrieval-Augmented Generation (RAG): Gain insights into RAG and its role in LLM optimization, enabling more accurate and efficient AI responses.
Leveraging LLM Tools for Summarization & Querying: Master using LLM tools for abstract summarization and querying, ensuring you can harness the full potential of large language models.
Why Enroll?
Guided by an expert instructor with an MPhil from the University of Oxford and a data-intensive PhD from Cambridge University, this course offers unparalleled expertise in LLM optimization. You'll benefit from a supportive learning environment, practical assignments, and a community of AI enthusiasts, ensuring a comprehensive understanding of LLM implementation.
Ready to Become an LLM Expert?
Enrol now to transform your AI capabilities, master LLM optimization techniques, and unlock the potential of text data with large language models. Join us and elevate your expertise in AI today!
Instructor Details
- 4.3 Rating
131 Reviews
Minerva Singh
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
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Reviews
By Èva Molnàr on 10/14/2026
I find it very disorganised, it deems to me that even the teacher does not have a clear understanding of what is going on, and it is just ridiculous that she is writing in code that is already there , she can jus comment that out and not wasting time.
By Matteo Marguerettaz on 7/23/2026
I prefer course made with a proper schedule instead of speaking off the cuff
By Suman Chemjong on 7/1/2026
A great course for anyone working with large language models. It helped me optimize AI systems for better speed and efficiency.
By Max Lewis on 6/30/2026
Highly recommended for those working with large language models and AI systems. It provides clear strategies to boost performance.
By Jack Hughes on 6/27/2026
I really enjoyed learning about AI optimization techniques. The course covers LLM performance tuning in a very structured way.
By Maria Rogers on 6/27/2026
This is one of the best courses for understanding how to improve the efficiency of large language models in real-world applications.
By Elias Clark on 6/27/2026
This course gave me a deep understanding of LLM optimization and how to improve AI performance. The concepts are explained clearly and practically.
By Jack Roberts on 6/27/2026
This course provided a clear and practical approach to LLM optimization. I learned how to improve AI performance, reduce latency, and make large language models more efficient for real-world applications.
By Jose Salomao on 5/17/2025
Incredible course—well-structured, fast-paced, and packed with practical knowledge you can instantly apply across various fields!
By Lester Aguilar on 5/17/2025
I found it incredibly rewarding to dive into a hands-on lab environment where I could test out various tools, see how they function in real-time, and apply what I learned right away.
Quality Score
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Overall Score : 86 / 100












