Large Language Models (LLMs) have revolutionized AI with their ability to understand and generate human-like text. Their rise is driven by advancements in deep learning, data availability, and computing power. Learning about LLMs is essential to harness their potential for solving complex language tasks and staying ahead in the evolving AI landscape. This article lists the top courses on LLMs that will help you gain a comprehensive understanding of LLMs, from basic concepts to advanced applications, ensuring you can effectively utilize these powerful tools in various AI-driven tasks.
Introduction to Large Language Models
Difficulty Level: Beginner
This course covers large language models (LLMs), their use cases, and how to enhance their performance with prompt tuning. This short course also includes guidance on using Google tools to develop your own Generative AI apps.
Prompt Engineering with LLaMA-2
Difficulty Level: Beginner
This course covers the prompt engineering techniques that enhance the capabilities of large language models (LLMs) like LLaMA-2. Students will learn to write precise prompts, edit system messages, and incorporate prompt-response history to create AI assistant and chatbot behavior.
Large Language Model Operations (LLMOps) Specialization
Difficulty Level: Beginner
This course from Coursera and Duke University offers comprehensive training in managing and deploying Large Language Models across platforms like Azure, AWS, and Databricks. It includes over 20 hands-on projects to gain practical experience in LLMOps, such as deploying models, creating prompts, and building chatbots.
ChatGPT Prompt Engineering for Developers
Difficulty Level: Beginner
This course teaches how to use OpenAI’s API for building powerful applications and custom chatbots, focusing on prompt engineering best practices. Participants will learn to summarize, infer, transform, and expand text with hands-on examples in a Jupyter Notebook environment.
Large Language Models (LLMs) Concepts
Difficulty Level: Beginner
This course explores Large Language Models (LLMs), their impact on AI, and real-world applications. It helps learn about LLM building blocks, training methodologies, and ethical considerations.
LangChain Chat with Your Data
Difficulty Level: Beginner
This course teaches Retrieval Augmented Generation and building chatbots that respond based on document content. It covers topics like document loading, splitting, vector stores, embeddings, retrieval techniques, question answering, and chatbot development using LangChain.
Introduction to LLMs in Python
Difficulty Level: Intermediate
This hands-on course teaches you to understand, build, and utilize Large Language Models (LLMs) for tasks like translation and question-answering. Students will learn to design transformer architectures, leverage pre-trained models from Hugging Face, and tackle real-world challenges and ethical considerations. This course also helps gain insights into practical skills through coding exercises and advanced concepts like Reinforcement Learning from Human Feedback (RLHF).
Foundations of Prompt Engineering
Difficulty Level: Intermediate
This course on prompt engineering covers principles, techniques, and best practices for designing effective prompts, including zero-shot and few-shot learning. It also addresses advanced techniques, identifying suitable prompts for specific models, preventing misuse, and mitigating bias in foundational model responses.
Generative AI with Large Language Models
Difficulty Level: Intermediate
This course teaches the fundamentals of generative AI with large language models (LLMs), including their lifecycle, transformer architecture, and optimization. It covers training, tuning, and deploying LLMs with practical insights from industry experts.
Generative AI and LLMs on AWS
Difficulty Level: Intermediate
This course teaches deploying generative AI models like GPT on AWS through hands-on labs, covering architecture selection, cost optimization, monitoring, CI/CD pipelines, and compliance. It is ideal for ML engineers, data scientists, and technical leaders, providing real-world training for production-ready generative AI using Amazon Bedrock and cloud-native services.
Inspect Rich Documents with Gemini Multimodality and Multimodal RAG
Difficulty Level: Intermediate
This course covers using multimodal prompts to extract information from text and visual data and generate video descriptions with Gemini. Participants learn to build metadata for documents containing text and images, retrieve relevant text chunks, and print citations using Multimodal RAG with Gemini.
Building RAG Agents with LLMs
Difficulty Level: Intermediate
This course explores the deployment and efficient implementation of large language models (LLMs) for enhanced productivity. Participants will learn to design dialog management systems, utilize embeddings for content retrieval, and implement advanced LLM pipelines using tools like LangChain and Gradio.
Generative Pre-trained Transformers (GPT)
Difficulty Level: Intermediate
This course introduces the fundamentals of natural language processing and language modeling, focusing on neural-based approaches like Transformers. Students learn about key innovations, ethical challenges, and hands-on labs for generating text with Python.
Generative AI and LLMs: Architecture and Data Preparation
Difficulty Level: Intermediate
This course teaches the basics of generative AI and Large Language Models (LLMs), covering architectures like RNNs, Transformers, GANs, VAEs, and Diffusion Models. It also covers tokenization methods, using data loaders with PyTorch, and applying Hugging Face libraries.
Finetuning Large Language Models
Difficulty Level: Intermediate
This course teaches finetuning concepts and training large language models using your data. Participants learn when to apply finetuning, how to prepare data, and how to train and evaluate LLMs.
Building Language Models on AWS
Difficulty Level: Advanced
This course on Amazon SageMaker targets experienced data scientists, focusing on building and optimizing language models. It covers storage, ingestion, and training options for large text corpora, along with deployment challenges and customization of foundational models for generative AI tasks using SageMaker Jumpstart.
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