The course is solid until the end, where you must wait for your peers evaluate your final paper. Seems like a bait and switch tactic and students such as myself may never complete the course as one waits for a peer to pick my paper. Furthermore, each student must evaluate four other peers' papers and I've evaluated six but yet I wait. There are folks in forums that are waiting for their final score for months. This is problematic and not the stated conditions when I agreed to take and pay for this course.

If you are familiar with high--level AI concepts and want a bit more detail to understand the application and the operations of machine learning, this is a great course.

The concepts build nicely from start to finish, the professors did an excellent job of explaining the material and the slides were a great visual supplement. This should all be basic blocking and tackling when teaching - but it's not.

I've taken other courses offered by higher education institutions and the "teaching" tends to be just a compilation of different reading exercises and random video interviews or discussions with professors - the material may have been interesting and informative, but it wasn't oriented to teaching.

With this course, I didn't have to work hard to grasp the basic concepts and instead had brain space to think more deeply about what I was hearing.

Excellent information and presentation. The only drawback in being so thought provoking, the format does not allow for active convesations in expanding understanding or ideas.

The AI Fundamentals for Non-Data Scientists course by University of Pennsylvania is an excellent resource for non-technical professionals looking to gain a comprehensive understanding of AI and its impact on businesses and industries. The course is entirely online, making it easy to learn at your own pace and in your own time. The material is presented in an engaging and easy-to-understand way. The instructors are knowledgeable, experienced, and passionate about the subject matter, providing prompt and helpful feedback. Overall, it's a well-structured, informative, and enjoyable learning experience that I highly recommend.

A very thorough introduction to application of AI and ML in a business context. I compelted the Stanford Machine Learning Specialization, which is quite technical and math-heavy, and this course was a perfect follow-on to put this learning in a real-world context. Easy to follow course content, accessible, well done.

suggest to have more real - life examples to complement theories. suggest to use more layman language on theory discussion.

great to have the presentation powerpoint available

I highly recommend AI Fundamentals for Non-Data Scientists course to anyone who wants to learn more about AI. It is well-designed and informative and provides a partial foundation for further study.

Here are some of the pros and cons of the course:

Pros:

Well-designed and informative

Covers a wide range of topics

Taught by Wharton professors and industry experts

Provides a solid foundation in the principles of AI

Includes interactive exercises and quizzes

Includes a capstone project

Cons:

The material can be challenging for beginners

Some of the material may be outdated

Not as hands-on as some other AI courses

Learned the basic concepts of using AI and Machine Learning in a business context. Understood what is deep learning, machine learning, structured and unstructured learning, feature engineering, decision trees, neural networks, etc.

Thank you, Professors Hosanger and Tambe, for being outstanding instructors! Your clear explanations, engaging teaching styles, and in-depth knowledge made the course enjoyable and informative.

Perfect level of technical detail to bring the concepts together for those who have some technical background. Course content was broken down into easily understood concepts.

Very rich in content course, truly learned a lot, clear explanations, crisp and informative slides. The ML application project adds strength to material understanding.

Helping me to clear doubts and i feel like getting required information. Thank you Coursera and Professinal Team.

Excellent introduction to the basic concepts of using AI and Machine Learning in a business context.

The format of this course of great for working parents who are complete novices about AI!

Modules 1 and 2 are older and were produced before the wide release and hype of ChatGPT, but they are still essential for Machine Learning and Data Science fundamentals, Module 3 and 5 get into Generative AI properly and have been updated more recently. Just push through the first 2 modules and stick with it - excellent course.

Great course and information. The quiz questions sometimes strayed from actual course content, in some cases referencing technology and tools not covered within. Also a bit dated given the last six months of progress, but a solid primer for people to qet acquainted with terms and application. Recommend.

Decent overview but needs some additional refresh for 2025

Outdated. The platform sucks for quizzes. Some questions come from later sections.

AI Fundamentals for Non‑Data Scientists by Wharton on Coursera deserves a full 5 stars. The course does exactly what it promises: it explains core AI and machine learning ideas in a way that a business professional or domain expert can understand and immediately connect to real decisions. The structure from fundamentals, to model evaluation, to deep learning, to generative AI and LLMs feels very intentional and builds confidence step by step rather than overwhelming you. The balance between theory and application is excellent. Short, focused videos explain key concepts like model selection, overfitting, loss functions, and different ML methods, and then tie them to business use cases and tools such as Teachable Machine, TensorFlow Playground, and AutoML. The generative AI module is a real highlight: it not only covers how foundation models and the generative AI stack work, but also shows how prompt design and product strategy can create competitive advantage for a company. What makes the course stand out is that it is truly designed for non‑data scientists. The maths stays light, but the explanations are still rigorous enough that you come away with a genuine understanding of what is happening under the hood. The quizzes, practical assignments, and peer‑reviewed project help to reinforce the ideas without requiring heavy coding. By the end, you have a clear mental model of how to scope AI projects, what data you need, how to think about model performance, and where AI adds value in areas like operations, marketing, finance, and productivity. Overall, this is an outstanding entry point into AI for managers, entrepreneurs, and professionals who need to lead or participate in AI initiatives but do not plan to become data scientists themselves. It is concise, well‑produced, and tightly aligned with real business needs, and it provides a strong foundation for the rest of the AI for Business specialization or for deeper technical study later.

Fantastic course as an introduction to applying machine learning and AI in a business context. Kartik Hosanagar and Prasanna Tambe deliver a clear, well-structured, and informative foundation for anyone looking to understand how these technologies can be leveraged in real-world decision-making. The examples are relevant, the concepts are well explained, and the content builds nicely across each module. Module 5 was a standout, offering an excellent breakdown of large language models and their practical applications. Highly recommend for professionals or students looking to bridge the gap between technical AI and business strategy.