NVIDIA, a global leader in artificial intelligence (AI) and GPU technology, has made all of its AI courses for free. The courses aim to equip individuals with the tools to thrive in today’s competitive job market.
NVIDIA is one of the most influential hardware giants in the world. Apart from its much sought-after GPUs, the company also provides free courses to help you understand more about generative AI, GPU, robotics, chips, and more.
The free online courses help learners gain critical AI, machine learning, and data science skills. These courses aim to equip individuals with the tools to thrive in today’s competitive job market.
All of the courses are available for free of cost and can be completed in less than a day. The courses are designed by NVIDIA’s industry experts which covers neural networks to LLMs and various AI domains.

Founded in 1993 by Jensen Huang, Chris Malachowsky, and Curtis Priem, NVIDIA is an American multinational corporation headquartered in Santa Clara, California. It specializes in designing and supplying graphics processing units (GPUs), application programming interfaces (APIs), and system-on-chip units (SoCs) for various industries, including gaming, data science, automotive, and high-performance computing.
NVIDIA leads the AI hardware and software market and dominates the GPU industry with an 80.2% market share in discrete desktop GPUs as of Q2 2023. The company also provides cloud computing solutions and products such as GeForce GPUs, NVIDIA Shield, and the GeForce Now gaming service.
Here is the list of NVIDIA’s free AI courses
1. Getting Started with AI
Here, participants will learn how to make & teach a computer to recognize different things using the NVIDIA Jetson Nano. This helps you create a set of examples for the computer to learn from, and then you’ll train it to tell things apart.
Click here to learn more about the course.
2. Accelerate Data Science Workflows with Zero Code Changes
Here, learners will learn how to build and execute end-to-end GPU-accelerated data science workflows for rapid data exploration and production deployment. Using RAPIDS™-accelerated libraries, one can apply GPU-accelerated machine learning algorithms, including XGBoost, cuGraph’s single-source shortest path, and cuML’s KNN, DBSCAN, and logistic regression.
Click here for more about the course.


3. Generative AI Explained
This self-paced, free online course introduces generative AI fundamentals, which involve creating new content based on different inputs. Through this course, participants will grasp the concepts, applications, challenges, and prospects of generative AI.
Learning objectives include defining generative AI and its functioning, outlining diverse applications, and discussing the associated challenges and opportunities. All participants need is a basic understanding of machine learning and deep learning principles.
Click here for more
4. Building a Brain in 10 Minutes
For this course, the context delves into neural networks’ foundations, drawing from biological and psychological insights. Its objectives are to elucidate how neural networks employ data for learning and to grasp the mathematical principles underlying a neuron’s functioning.
While anyone can execute the code provided to observe its operations, a solid grasp of fundamental Python 3 programming concepts (including functions, loops, dictionaries, and arrays) is required. Also, familiarity with computing regression lines is also recommended.
For more details on the course, click here.


5. Augment your LLM Using Retrieval Augmented Generation (RAG)
Retrieval Augmented Generation (RAG), devised by Facebook AI Research in 2020, offers a method to enhance an LLM output by incorporating real-time, domain-specific data, eliminating the need for model retraining. RAG integrates an information retrieval module with a response generator, forming an end-to-end architecture.
Being an introductory course, it provides a foundational understanding of RAG, including its retrieval mechanism and the essential components within NVIDIA’s AI Foundations framework. By grasping these fundamentals, you can initiate your exploration into LLM and RAG applications.
Explore more on the course here.
6. Building RAG Agents with LLMs
This course is designed for those eager to explore the potential of these systems, focusing on practical deployment and the efficient implementation required to manage the considerable demands of both users and deep learning models.
Learn more about the course here.
7. Building Video AI Applications at the Edge on Jetson Nano
This self-paced online course aims to equip learners with skills in AI-based video understanding using the NVIDIA Jetson Nano Developer Kit. Through practical exercises and Python application samples in JupyterLab notebooks, participants will explore intelligent video analytics (IVA) applications leveraging the NVIDIA DeepStream SDK.
The course covers setting up the Jetson Nano, constructing end-to-end DeepStream pipelines for video analysis, integrating various input and output sources, configuring multiple video streams, and employing alternate inference engines like YOLO.
Prerequisites include basic Linux command line familiarity and an understanding of Python 3 programming concepts. For this course, participants will require hardware including the NVIDIA Jetson Nano Developer Kit or the 2GB version, along with a compatible power supply, microSD card, USB data cable, and a USB webcam. Access to the course ends by 2/4/2025.
To learn more in detail about the course, check here.
Also Read: Nvidia briefly tops Apple, Microsoft as world’s most valuable company with $3.53trn valuation.