Top Large Language Models LLMs Courses
Introduction to Large Language Models
This course covers large language models (LLMs), their use cases, and how to enhance their performance with prompt tuning. It also includes guidance on using Google tools to develop your own Generative AI apps.
Prompt Engineering with LLaMA-2
This course covers the prompt engineering techniques that enhance the capabilities of large language models (LLMs) like LLaMA-2, focusing on creating AI assistant and chatbot behavior.
Large Language Model Operations (LLMOps) Specialization
This course 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.
ChatGPT Prompt Engineering for Developers
This course teaches how to use OpenAI’s API for building powerful applications and custom chatbots, focusing on prompt engineering best practices.
Large Language Models (LLMs) Concepts
This course explores Large Language Models (LLMs), their impact on AI, and real-world applications, helping to learn about LLM building blocks, training methodologies, and ethical considerations.
LangChain Chat with Your Data
This course teaches Retrieval Augmented Generation and building chatbots that respond based on document content, covering various topics related to chatbot development using LangChain.
Introduction to LLMs in Python
This hands-on course teaches to understand, build, and utilize Large Language Models (LLMs) for tasks like translation and question-answering, including practical skills through coding exercises and advanced concepts like Reinforcement Learning from Human Feedback (RLHF).
Foundations of Prompt Engineering
This course covers principles, techniques, and best practices for designing effective prompts, including zero-shot and few-shot learning, and advanced techniques for identifying suitable prompts for specific models.
Generative AI with Large Language Models
This course teaches the fundamentals of generative AI with large language models (LLMs), including their lifecycle, transformer architecture, and optimization, along with practical insights from industry experts.
Generative AI and LLMs on AWS
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, 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
This course covers using multimodal prompts to extract information from text and visual data and generate video descriptions with Gemini, focusing on building metadata for documents containing text and images, and retrieval techniques using Multimodal RAG with Gemini.
Building RAG Agents with LLMs
This course explores the deployment and efficient implementation of large language models (LLMs) for enhanced productivity, covering the design of dialog management systems, utilizing embeddings for content retrieval, and implementing advanced LLM pipelines using tools like LangChain and Gradio.
Generative Pre-trained Transformers (GPT)
This course introduces the fundamentals of natural language processing and language modeling, focusing on neural-based approaches like Transformers, along with hands-on labs for generating text with Python.
Generative AI and LLMs: Architecture and Data Preparation
This course teaches the basics of generative AI and Large Language Models (LLMs), covering architectures like RNNs, Transformers, GANs, VAEs, and Diffusion Models, along with tokenization methods, using data loaders with PyTorch, and applying Hugging Face libraries.
Finetuning Large Language Models
This course teaches finetuning concepts and training large language models using your data, including the preparation, training, and evaluation of LLMs.
Building Language Models on AWS
This course on Amazon SageMaker targets experienced data scientists, focusing on building and optimizing language models, covering 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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