Best Free AI Courses with Certificates

With the rise of accessible AI tools like ChatGPT, there has never been a better time to learn about artificial intelligence. Whether you're a complete beginner or looking to expand your knowledge, free online courses with certificates offer valuable insights and practical skills to help you navigate the exciting world of AI. The availability of these courses from reputable providers like Learn Prompting and NVIDIA Deep Learning Institute demonstrates the increasing accessibility of AI education. NVIDIA Deep Learning Institute, for example, offers a variety of free courses for all levels, from beginners to experienced professionals.

Looking for the perfect free AI course? We've got you covered! Check out our summary table below for a quick overview of the top options:

Course Title	Provider	Length	Topics Covered	Prerequisites	Certificate Type	Reviews
ChatGPT for Everyone	Learn Prompting	1 hour	ChatGPT basics, prompt engineering, DALL-E 3 basics, AI safety	None	Completion Certificate	Highly positive, over 82,000 learners enrolled. Learners praise the course's clear explanations and practical focus, with one reviewer stating, "This course is packed with practical insights and hands-on applications to make ChatGPT your ultimate productivity tool."
Introduction to Artificial Intelligence	Great Learning	1.5 hours	AI foundations, neural networks, NLP, computer vision	None	Completion Certificate	Positive, over 160,000 learners enrolled. Learners appreciate the comprehensive coverage and clear explanations, with one reviewer stating, "This course provided a solid foundation in AI concepts, covering topics from machine learning to natural language processing."
Generative AI Explained	NVIDIA Deep Learning Institute	2 hours	Generative AI fundamentals, applications, challenges, and opportunities	None	Certificate of Completion	Positive, praised for clear explanations and engaging content. One reviewer commented, "Generative AI Explained is a beginner-friendly introduction to generative AI fundamentals to get your feet wet."
Building RAG Agents for LLMs	NVIDIA Deep Learning Institute	8 hours	Building LLM applications with RAG, prompt engineering, tools like LangChain and LangServe	Intermediate Python programming experience, some PyTorch experience	Certificate of Completion	Positive, particularly for the coding exercises for the RAG framework. One learner shared, "I tried a couple of them and they are pretty good, especially the coding exercises for the RAG framework (how to connect external files to an LLM)."
Getting Started with AI on Jetson Nano	NVIDIA Deep Learning Institute	8 hours	Setting up Jetson Nano, image classification, image regression	Basic familiarity with Python	Certificate of Completion	Positive, well-structured with hands-on projects. Learners appreciate the comprehensive guidance and practical exercises.

1. ChatGPT for Everyone by Learn Prompting

Developed in collaboration with OpenAI, "ChatGPT for Everyone" provides a comprehensive introduction to ChatGPT and its applications. This one-hour, self-paced course covers the fundamentals of ChatGPT, including setting up an account, writing effective prompts, and exploring practical use cases in various domains. The course highlights the crucial role of prompt engineering in maximizing the potential of large language models like ChatGPT.

Course Content

The course is structured into five modules:

• Introduction to ChatGPT: This module introduces the concept of ChatGPT, its underlying mechanisms, and its potential applications.
• Getting Started: This module guides learners through setting up a ChatGPT account, writing their first prompt, and exploring the platform's features.
• ChatGPT Use Cases: This module delves into practical applications of ChatGPT, such as everyday writing, brainstorming, and personalized learning.
• Basics of Prompt Crafting: This module teaches learners how to structure prompts effectively to generate high-quality responses from ChatGPT.
• AI Safety & Limitations: This module explores the ethical considerations and limitations of ChatGPT, emphasizing responsible AI usage.

Pros

• Beginner-friendly: The course uses clear language and avoids technical jargon, making it accessible to learners with no prior AI experience.
• Practical focus: The course emphasizes hands-on exercises and real-world applications, allowing learners to apply their knowledge immediately.
• Interactive format: The course incorporates quizzes and interactive elements to keep learners engaged.
• Expert instructors: The course is developed and taught by leading AI practitioners, including Sander Schulhoff, Founder & CEO of Learn Prompting, and Shyamal Anadkat, Applied AI expert at OpenAI.

Cons

• Limited scope: As an introductory course, it may not cover advanced topics in AI or ChatGPT.

Who is this course best suited for?

This course is ideal for beginners, professionals seeking to enhance productivity, AI enthusiasts, and anyone curious about using ChatGPT for various tasks.

2. Introduction to Artificial Intelligence by Great Learning

This free course provides a foundational understanding of artificial intelligence, covering key concepts, techniques, and applications. It explores the core components of AI, including neural networks, natural language processing (NLP), and computer vision. This 1.5-hour course is instructed by Dr. Abhinanda Sarkar and covers skills including Generative AI, Prompt Engineering, ChatGPT, Explainable AI, Machine Learning Algorithms, and more.

Course Content

The course is divided into five modules:

• Introduction to AI: This module provides an overview of AI and its applications in various domains.
• Neural Networks: This module explores the fundamentals of neural networks, including artificial neural networks, biological neurons, and deep neural networks.
• Introduction to Natural Language Processing: This module introduces NLP and its applications, such as sentiment analysis and chatbots.
• Introduction to Computer Vision: This module covers the basics of computer vision, including image classification, segmentation, and video analysis.
• Computer Vision Examples: This module provides practical examples of computer vision applications, such as image classification, face recognition, and video and traffic analytics.

Pros

• Comprehensive coverage: The course covers a wide range of AI topics, providing a solid foundation for beginners.
• Clear explanations: The course uses simple language and real-world examples to explain complex AI concepts.
• Certificate of completion: Learners receive a certificate upon completing the course, which can be valuable for career advancement.

Cons

• Limited practical exercises: While the course includes some examples, it may not provide as many hands-on exercises as some learners might prefer.

Who is this course best suited for?

This course is best suited for beginners with no prior AI experience who want to gain a broad understanding of the field.

3. Generative AI Explained by NVIDIA Deep Learning Institute

This free course provides a comprehensive introduction to generative AI, covering its fundamentals, applications, challenges, and opportunities. It explores how generative AI models work, their use cases in various industries, and the ethical considerations surrounding their development and deployment. The NVIDIA Deep Learning Institute emphasizes hands-on learning and practical application, providing learners with the skills and knowledge to understand and implement real-world AI solutions.

Course Content

The course covers the following topics:

• Generative AI and how it works: This module explains the basic concepts of generative AI and the different types of generative models.
• Generative AI applications: This module explores the diverse applications of generative AI in areas such as image generation, text generation, and drug discovery.
• Challenges and opportunities in Generative AI: This module discusses the ethical considerations, limitations, and potential risks associated with generative AI, as well as the opportunities it presents for innovation and problem-solving.

Pros

• No coding required: This course is designed for learners with no prior coding experience, making it accessible to a wider audience.
• Concise and informative: The course delivers key concepts and information in a clear and concise manner.
• Self-paced learning: Learners can progress through the course at their own speed, allowing for flexibility and convenience.

Cons

• Limited depth: As an introductory course, it may not cover advanced topics or provide extensive hands-on experience.

Who is this course best suited for?

This course is ideal for beginners, business professionals, and anyone interested in understanding the fundamentals and potential of generative AI.

4. Building RAG Agents for LLMs by NVIDIA Deep Learning Institute

This free course delves into the practical application of Retrieval Augmented Generation (RAG) for building LLM applications. It explores how to enhance Large Language Models with external knowledge sources, improve their accuracy and reliability, and create more informative and engaging AI systems. The course provides a balance of theoretical knowledge and practical implementation techniques.

Course Content

The course covers the following topics:

• LLM pipeline design: This module explores the process of designing LLM pipelines for RAG applications, including data preparation, retrieval, and response generation.
• Tools for RAG development: This module introduces various tools and libraries for building RAG applications, such as LangChain, LangServe, and Gradio.
• Embeddings and vector stores: This module explains the concept of embeddings and how to use vector stores for efficient retrieval of relevant information.
• Prompt engineering for RAG: This module provides guidance on crafting effective prompts for RAG applications to generate high-quality responses.

Pros

• Hands-on experience: The course includes coding exercises and practical examples, allowing learners to apply RAG techniques and build their own LLM applications.
• Focus on real-world applications: The course emphasizes the use of RAG in practical scenarios, such as question answering, summarization, and chatbot development.
• Free and self-paced: Learners can access the course materials and progress at their own convenience.

Cons

• Requires intermediate programming skills: Learners should have a solid understanding of Python and some experience with PyTorch to fully benefit from the course.

Who is this course best suited for?

This course is best suited for developers and AI enthusiasts with intermediate programming skills who want to build LLM applications with RAG.

5. Getting Started with AI on Jetson Nano by NVIDIA Deep Learning Institute

This free course provides a hands-on introduction to AI using the NVIDIA Jetson Nano Developer Kit. It guides learners through setting up the Jetson Nano, collecting and annotating image data, and training and deploying AI models for image classification and regression tasks. The course combines theoretical concepts with practical implementation on hardware specifically designed for AI applications.

Course Content

The course covers the following topics:

• Setting up your Jetson Nano: This module provides step-by-step instructions for setting up the Jetson Nano Developer Kit and configuring the necessary software.
• Image classification: This module introduces the concepts of AI and deep learning, convolutional neural networks (CNNs), and the ResNet-18 network architecture. Learners build image classification projects using interactive Jupyter notebooks.
• Image regression: This module explores image regression techniques and guides learners through building a project to predict the steering angle of a self-driving car.
• Running inference on the Jetson Nano: This module explains how to deploy and run AI models on the Jetson Nano for real-time applications.

Pros

• Hands-on learning: The course emphasizes practical exercises and projects, allowing learners to gain experience with AI on a real-world embedded platform.
• Comprehensive guidance: The course provides clear instructions and resources for setting up the Jetson Nano and completing the projects.
• Free and self-paced: Learners can access the course materials and progress at their own speed.

Cons

• Requires specific hardware: Learners need to have access to an NVIDIA Jetson Nano Developer Kit to complete the course.
• Assumes basic Python familiarity: While not strictly required, some familiarity with Python is helpful for understanding the code and concepts.

Who is this course best suited for?

This course is best suited for those interested in hands-on experience with AI on embedded systems, particularly those with access to a Jetson Nano and some basic Python knowledge.

Conclusion

These free AI courses offer a valuable opportunity to learn about artificial intelligence and gain practical skills in this rapidly evolving field. They represent a growing trend in making AI education more accessible to a wider audience, regardless of background or prior experience. When choosing a course, consider your learning goals, preferred learning style, and any prerequisites that may be required.