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Large Language Models (LLMs) are a hot topic right now, and everyone is getting involved in this new trend. Companies are searching for LLM engineers who can develop and implement AI solutions to optimize their workflow and reduce costs through automation, customer service, recommendations, issue resolution, and debugging. Instead of worrying that AI will take your job, why not upskill and join the race?
In this blog, we will provide a review of free courses, guides, lists, roadmaps, boot camps, and tutorials that will help you get started with LLMs and master them within 3 months. You will learn the basics of building LLMs and then delve deeper into building context-aware applications.
1. LLM University by Cohere
LLM University (LLMU) by Cohere offers a comprehensive set of learning resources, expert-led courses, and step-by-step guides to help you start building AI applications. The course consists of 8 modules covering LLMs, test representation, text generation, deployments, semantic search, Prompt engineering, Retrieval-Augmented Generation (RAG), and Tool use. This course is suitable for both beginners and advanced learners, covering everything from the basics to advanced concepts in LLMs.
2. CS324 – Large Language Models by Stanford University
Stanford University’s CS324 course is a deep dive into the world of LLMs. This course covers the theoretical foundations and practical applications of LLMs, making it an excellent resource for those looking to understand the inner workings of these models. In this course, students will learn the fundamentals of modeling, theory, ethics, and systems aspects of large language models and gain hands-on experience working with them.
3. Large Language Models Roadmap
The Large Language Models Roadmap by Maxime Labonne is a GitHub repository that offers a structured approach to learning about LLMs. It includes roadmaps, Colab notebooks, and a variety of resources to help you get hands-on experience with LLMs.
The LLM course is divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. These modules will help you understand the math behind LLMs, assist you in building your own LLMs using the latest techniques, and enable you to develop and deploy AI applications.
4. Full Stack LLM Bootcamp
The Full Stack LLM Bootcamp by industrial experts started as a deep learning bootcamp and has evolved into a comprehensive LLM bootcamp. You will learn about best practices and tools, covering prompt engineering to user-centered design and state-of-the-art LLM solutions. In short, you will learn how to build LLMs, integrate them with external data resources, and deploy the solutions.
5. DataCamp Tutorials
DataCamp offers a variety of AI tutorials, including those focused on LLMs. These tutorials come with guides on building and fine-tuning LLMs, creating context-aware AI applications, and deploying solutions. You will learn about both open-source and proprietary models and tools. Each tutorial can be considered a project that comes with a code source, which you can add to your resume. The DataCamp tutorials are a treasure trove of LLM knowledge.
6. Awesome-LLM
The Awesome-LLM GitHub repository is a curated list of resources related to LLMs. It includes links to tutorials, papers, tools, and other resources to help you learn about and work with LLMs. This repository is regularly updated and provides information for anyone interested in LLMs.
The list is divided into various topics such as Milestone Papers, LLM Leaderboard, Open LLM, LLM Data, LLM Evaluation, LLM Training Framework, LLM Deployment, LLM Applications, LLM Books, and more.
7. Generative AI for Beginners by Microsoft
Generative AI for Beginners by Microsoft is a comprehensive course that includes 18 lessons on building with generative AI. This course is designed for beginners and provides a solid introduction to the concepts and techniques used in generative AI, including LLMs. The lessons are available online for free, making it an accessible resource for anyone looking to get started with LLMs.
The course covers lessons on Introduction to Generative AI and LLMs, Exploring and comparing different LLMs, Using Generative AI Responsibly, Understanding Prompt Engineering Fundamentals, Building Text Generation Applications, Building Search Apps Vector Databases, Securing Your Generative AI Applications, and more.
Conclusion
Free resources are always a gateway into the world of any field. They will help you learn the basics and even help you build a strong portfolio that will land you a job. The free courses and resources that we have discussed in this blog are top-of-the-line, and anyone can start learning about them and using them to build AI solutions. The main purpose of these resources is learning, but the ultimate goal is to have hands-on experience in building real-life applications that actually work in production.