Large language models (LLMs) courses cover topics like natural language processing, text generation, and model training. You’ll learn about the architectures behind models such as GPT and BERT, how they process and generate text, and the implications of their use. Expect to explore methods for fine-tuning these models and applying them in different contexts.

Skills you'll gain: Large Language Modeling, LLM Application, Fine-tuning, Model Deployment, Model Training, Transfer Learning, Generative Model Architectures, Prompt Engineering, Model Optimization, Generative AI, AI Integrations, Model Evaluation, Deep Learning, Natural Language Processing, Recurrent Neural Networks (RNNs), AI Security, Responsible AI, Scalability, Machine Learning
Advanced · Course · 3 - 6 Months

Johns Hopkins University
Skills you'll gain: Statistical Hypothesis Testing, Regression Analysis, Statistical Analysis, Probability & Statistics, Statistical Inference, Statistical Methods, Statistical Modeling, Linear Algebra, Statistics, Probability, R Programming, Biostatistics, Data Analysis, Data Science, Probability Distribution, Derivatives
★ 4.4 (802) · Advanced · Specialization · 3 - 6 Months

Skills you'll gain: PyTorch (Machine Learning Library), Fine-tuning, Convolutional Neural Networks, Deep Learning, Natural Language Processing, Embeddings, Hugging Face, Supervised Learning, Classification Algorithms, Data Preprocessing, Predictive Modeling, Machine Learning, Data Processing, Artificial Intelligence and Machine Learning (AI/ML), Statistical Methods, Probability & Statistics, Machine Learning Algorithms
★ 4.4 (2.1K) · Advanced · Specialization · 3 - 6 Months

Duke University
Skills you'll gain: Fine-tuning, MLOps (Machine Learning Operations), Model Deployment, Cloud Deployment, Pandas (Python Package), AWS SageMaker, NumPy, Microsoft Azure, Hugging Face, GitHub Copilot, Responsible AI, Data Manipulation, DevOps, Cloud Computing, Python Programming, Machine Learning, GitHub, Big Data, Data Management, Data Analysis
★ 4.1 (654) · Advanced · Specialization · 3 - 6 Months

Skills you'll gain: Prompt Engineering, AI Orchestration, AI Workflows, LangGraph, Agentic Workflows, LangChain, Retrieval-Augmented Generation, LLM Application, Tool Calling, Agentic systems, Model Context Protocol, Generative AI Agents, Generative AI, AI Security, Vector Databases, Responsible AI, Embeddings, Large Language Modeling, AI Integrations, Software Development
★ 4.6 (1.2K) · Advanced · Professional Certificate · 3 - 6 Months

Amazon Web Services
Skills you'll gain: Prompt Engineering, Retrieval-Augmented Generation, Personally Identifiable Information, Dashboard Creation, Token Optimization, Generative AI Agents, Responsible AI, AI Workflows, Generative AI, Agentic systems, Enterprise Architecture, API Design, API Gateway, Performance Tuning, Amazon Web Services, Artificial Intelligence and Machine Learning (AI/ML), Cloud Applications, Artificial Intelligence, Cloud Platforms, Machine Learning
Advanced · Professional Certificate · 3 - 6 Months

Skills you'll gain: Feature Engineering, Model Deployment, Data Visualization, Data Ethics, Exploratory Data Analysis, Model Evaluation, Unsupervised Learning, Data Presentation, Tensorflow, Application Deployment, Dimensionality Reduction, Model Training, Probability Distribution, Apache Spark, Statistical Hypothesis Testing, Supervised Learning, Design Thinking, Data Science, Machine Learning, Python Programming
★ 4.4 (399) · Advanced · Specialization · 3 - 6 Months

ESSEC Business School
Skills you'll gain: Data-Driven Marketing, Marketing Analytics, Business Analytics, Forecasting, Peer Review, Statistical Methods, Data Presentation, Customer Analysis, Case Studies, R (Software), Information Technology, Presentations, Analytics, Digital Transformation, Advanced Analytics, Marketing Strategies, Complex Problem Solving, Analysis, R Programming, Data Analysis
★ 4.4 (1.3K) · Advanced · Specialization · 3 - 6 Months

University of Colorado Boulder
Skills you'll gain: Bash (Scripting Language), Distributed Computing, Scalability, Memory Management, Computer Systems, Computer Science, File I/O, Big Data, File Systems, Scripting, Performance Tuning, Linux, C++ (Programming Language), C (Programming Language)
★ 3.8 (190) · Advanced · Specialization · 3 - 6 Months

Google Cloud
Skills you'll gain: Google Gemini, Retrieval-Augmented Generation, Model Deployment, Google Cloud Platform, Generative AI, LLM Application, MLOps (Machine Learning Operations), Predictive Analytics, Embeddings, Predictive Modeling, Data Analysis, Time Series Analysis and Forecasting, Forecasting, Applied Machine Learning, Model Training, Data Preprocessing, Model Evaluation, Machine Learning, Classification Algorithms, SQL
★ 4.6 (1.3K) · Advanced · Specialization · 3 - 6 Months

Amazon Web Services
Advanced · Course · 1 - 4 Weeks
Start with what you want to do. If you want to implement conversational agents, choose courses focusing on dialogue systems and natural language understanding. If you're interested in Hugging Face Transformers, check for courses that cover model fine-tuning and deployment using that library. Compare these options to find the best fit for your needs.
Top-rated Large Language Models courses offered by IBM on Coursera.
Earn a certificate in Large Language Models from top universities and companies.
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.‎