Large Language Models courses can help you learn natural language processing, text generation techniques, and model evaluation methods. You can build skills in fine-tuning models, understanding tokenization, and implementing ethical AI practices. Many courses introduce tools like TensorFlow and PyTorch, along with libraries such as Hugging Face Transformers, that support developing and deploying AI applications that leverage large language models.

Google Cloud
Skills you'll gain: Large Language Modeling, Google Gemini, Prompt Engineering, LLM Application, Generative AI
Beginner · Course · 1 - 4 Weeks

DeepLearning.AI
Skills you'll gain: Generative AI, Large Language Modeling, Generative Model Architectures, LLM Application, Prompt Engineering, Model Deployment, Python Programming, Applied Machine Learning, Scalability, Natural Language Processing, Responsible AI, Machine Learning, Model Evaluation, Reinforcement Learning
Intermediate · Course · 1 - 4 Weeks

Duke University
Skills you'll gain: Prompt Engineering, Databricks, Large Language Modeling, Model Deployment, LLM Application, Generative AI, Performance Analysis, Retrieval-Augmented Generation, Generative Model Architectures, Apache Airflow, Workflow Management, Hugging Face, Amazon Bedrock, Vector Databases, Data Lakes, ChatGPT, Extract, Transform, Load, OpenAI, Multimodal Prompts, MLOps (Machine Learning Operations)
Beginner · Specialization · 3 - 6 Months

DeepLearning.AI
Skills you'll gain: Large Language Modeling, Prompt Engineering, Retrieval-Augmented Generation, Transfer Learning, Data Preprocessing
Intermediate · Project · Less Than 2 Hours

Vanderbilt University
Skills you'll gain: Prompt Engineering, ChatGPT, Prompt Patterns, LLM Application, Productivity, OpenAI, AI Enablement, Generative AI, Artificial Intelligence, Large Language Modeling, Creativity, Problem Solving, Context Management, Verification And Validation
Beginner · Course · 1 - 3 Months

Skills you'll gain: Prompt Engineering, Apache Spark, Large Language Modeling, Transfer Learning, PyTorch (Machine Learning Library), Model Evaluation, Computer Vision, Retrieval-Augmented Generation, Unsupervised Learning, Generative Model Architectures, Generative AI, PySpark, Vision Transformer (ViT), Keras (Neural Network Library), LLM Application, Supervised Learning, Vector Databases, Machine Learning, Python Programming, Data Science
Build toward a degree
Intermediate · Professional Certificate · 3 - 6 Months

Skills you'll gain: Prompt Engineering, Large Language Modeling, Generative AI, Retrieval-Augmented Generation, Generative Model Architectures, PyTorch (Machine Learning Library), Vector Databases, LLM Application, Generative Adversarial Networks (GANs), Embeddings, Natural Language Processing, Hugging Face, Transfer Learning, Data Pipelines, Recurrent Neural Networks (RNNs), Text Mining, Data Ethics, Data Preprocessing, Artificial Intelligence, Performance Tuning
Intermediate · Specialization · 3 - 6 Months

Skills you'll gain: Generative AI, Generative Model Architectures, Large Language Modeling, LLM Application, Generative Adversarial Networks (GANs), Retrieval-Augmented Generation, OpenAI, Hugging Face, Multimodal Prompts, Responsible AI, Embeddings, Prompt Engineering, AI Security, Autoencoders, Vision Transformer (ViT), Model Deployment, Image Analysis
Intermediate · Course · 1 - 3 Months

H2O.ai
Skills you'll gain: Large Language Modeling, Model Evaluation, Collaborative Software, Artificial Neural Networks, Deep Learning, Artificial Intelligence and Machine Learning (AI/ML), Generative AI, LLM Application, Data Preprocessing, Prompt Engineering, Generative Model Architectures, Training Programs, Data Validation, Data Cleansing, Natural Language Processing, Applied Machine Learning, Transfer Learning, Verification And Validation, Data Quality, AI Workflows
Intermediate · Specialization · 3 - 6 Months

Skills you'll gain: Prompt Engineering, Exploratory Data Analysis, Prompt Patterns, LangChain, Large Language Modeling, Retrieval-Augmented Generation, Model Evaluation, Unsupervised Learning, Generative Model Architectures, PyTorch (Machine Learning Library), ChatGPT, Generative AI, Restful API, LLM Application, Keras (Neural Network Library), Data Transformation, Supervised Learning, Responsible AI, Vector Databases, Data Import/Export
Beginner · Professional Certificate · 3 - 6 Months

DeepLearning.AI
Skills you'll gain: LLM Application, Large Language Modeling, Embeddings, Prompt Engineering, Data Processing, Big Data, Applied Machine Learning, Vector Databases
Beginner · Project · Less Than 2 Hours

Skills you'll gain: Prompt Engineering, Large Language Modeling, LLM Application, Retrieval-Augmented Generation, ChatGPT, Natural Language Processing, Generative AI Agents, OpenAI API, Model Deployment, Embeddings, Transfer Learning, Generative Model Architectures, Multimodal Prompts, Generative AI, Cloud Deployment, Responsible AI, Artificial Intelligence and Machine Learning (AI/ML), AI Personalization, Cost Management, Machine Learning
Intermediate · Specialization · 1 - 4 Weeks
Large language models (LLMs) are advanced artificial intelligence systems designed to understand and generate human-like text. They utilize vast amounts of data and sophisticated algorithms to learn patterns in language, enabling them to perform a variety of tasks, such as translation, summarization, and content creation. The importance of LLMs lies in their ability to enhance communication, automate processes, and provide insights across numerous fields, including education, healthcare, and business. As organizations increasingly rely on data-driven decision-making, understanding LLMs becomes essential for leveraging their capabilities effectively.
Careers in large language models are diverse and growing rapidly. You might consider roles such as AI Research Scientist, Machine Learning Engineer, Data Scientist, or Natural Language Processing (NLP) Specialist. These positions often involve developing and implementing LLMs for various applications, including chatbots, recommendation systems, and content generation tools. Additionally, roles in product management and AI ethics are emerging as organizations seek to responsibly integrate LLMs into their operations. With the right skills and knowledge, you can position yourself for a rewarding career in this dynamic field.
To work effectively with large language models, you should focus on acquiring a blend of technical and analytical skills. Key areas include programming languages such as Python, familiarity with machine learning frameworks like TensorFlow or PyTorch, and a solid understanding of natural language processing concepts. Additionally, knowledge of data handling, model evaluation, and ethical considerations in AI is crucial. Courses that cover these topics can help you build a strong foundation and prepare you for practical applications in the field.
There are several excellent online courses available for learning about large language models. Notable options include the Large Language Models Specialization, which provides a comprehensive overview of LLMs, and the Generative AI and Large Language Models course, focusing on practical applications. For those interested in a structured learning path, the Quick Start Guide to Large Language Models (LLMs) Specialization is also a great choice.
Yes. You can start learning large language models on Coursera for free in two ways:
If you want to keep learning, earn a certificate in large language models, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.
To learn about large language models, start by identifying your current skill level and areas of interest. You can begin with introductory courses, such as the Introduction to Large Language Models, which provide a solid foundation. As you progress, consider more specialized courses that focus on specific applications or technologies. Engage with hands-on projects to apply what you learn, and participate in online communities to connect with others in the field. This approach will help reinforce your understanding and build your confidence.
Courses on large language models typically cover a range of topics, including the fundamentals of natural language processing, the architecture of LLMs, training techniques, and evaluation methods. You may also explore practical applications, such as building chatbots, content generation, and ethical considerations in AI. Advanced courses might explore into specific frameworks and tools used in the industry, providing you with the skills needed to implement LLMs effectively.
For training and upskilling employees or the workforce in large language models, consider courses like the Building Production-Ready Apps with Large Language Models course, which focuses on practical implementation. Additionally, the H2O AI Large Language Models (LLMs) - Level 1 course provides foundational knowledge that can be beneficial for teams looking to integrate LLMs into their projects. These courses can help organizations enhance their capabilities and stay competitive in the evolving landscape of AI.