This course is WAY too technical. If you are a data scientist, this would be more understandable, but as a Product Manager, it went way over my head.

At the end of the course, the project is far beyond my understanding, and I had to give up. :-(

the instructor is reading from a slide,it is not a well prepared course

A lot of good content, but not a great presentation/organization making it hard to be engaging. Especially for working professionals, the presenter's energy level does not motivate them to keep going. You are better off doing a proper AI/ML course instead.

A very good - and technical- course. It's a bit misleading the refer to this course as "Beginner" level. It's a bit more than that. The one VERY BIG suggestion I have is about the final project. The course DOES NOT provide any support to prepare "beginners" for that project. In fact, some people withdrew from the course in anger over this. What Duke should do is provide a demonstration of how to do that work, whether in Excel or Google or any other tool. I had NO IDEA how to begin. So I spent literally hours looking for videos that demonstrated it. I persevered and got it done, but I think Duke should do more for people who are paying for this. It is really unfortunate to do all that course work and then withdraw at the every end because you lack the support and guidance to complete the final project. PLEASE pass this on to Jon Reifschneider.

Great course and even more applications exemples would be even better :)

Not a bad course but I'm not sure how this it relates to Product Management, aside from some industry examples in the content. For my knowledge level, this was way too dense. Loads of formulas and modeling that I'll never use. The course lost me when it started writing out mathematic equations. I'm finishing the course because of sunk cost bias but would not take it again seeing how the content does not match up with any area of my job.

Interesting course with a lot of potential, but 3 major feedback points soured it for me: 1. Although this course is explicitly "for Product Managers" in its title, there is no mention of product management or anything specifically relevant to PMs in the entire course. It is really more of a generalist course for anyone. Had I known this, I would have more fully evaluated the complete ML foundations course landscape. 2. The AutoML platform recommended for the final project was sunset by Google, and there's no helpful guide to using VertexAI as its replacement. I and other students (based on the forums) have spent hours and hours trying to debug Vertex errors to no avail. 3. Week 6 suddenly and unnecessarily goes very hardcore into math and calculus relating to neural networks in a very fast pace, without actually explaining or teaching what any of it means.

The course itself was quite good, a thorough introduction to machine learning. So why the two stars? For the final project, the course offers three possible methods: programming in Python, via VBA in Excel, or using Google Cloud AI. The first two were not an option for me, as I do not code. What I wish I knew was that, in order to make Google Cloud feasible, I would have to spend hundreds of dollars on hosting the (relatively modest) dataset. The course description was not at all clear about this.

This course is well structured, covering a lot of what is required high level to discover ML.

Though the level of math required is too high. I don't think this course is for beginners.

This is a really good course that provides a solid overview of machine learning and some of the primary methods for doing so. As a product manager, I would say there should be a little less math in the lectures because it distracts from the essence of what you are trying to teach. Overall, a very good course.

Too technical. The quizzes don't align well to the lectures, and the lectures are simply way too technical for most to grasp. The capstone item is absurd for a certificate. I don't recommend this course.

Excellent introduction to product management for machine learning. It covers the basics so you can understand the language and terminology of machine learning. Final project wasn't very relatable to the content but was useful in helping design a basic regression model which is just a heuristic model and truly a machine learning model. All in all it was well worth the time and effort and you do learn a lot if you are new to machine learning applications and projects.

It is a good introduction into machine learning concepts that finds the right balance between required depth and and time efficient knowledge transfer.

As the title indicates, it is a good introduction on management level and is not suited to train data scientists.

A negative point: The instructor speaks incredibly slow and is rather unenthusiastic. However putting the speed on 1.5-2 times fixes this.

Excellent course material ,well-structured course work and detailed instructions. Explaining detailed algorithms was really helpful in understanding the core concepts.

Additional information on industry best practices and case studies with industry experts would be more helpful as the course evolves in the coming days.Nice work and thank you!

Awesome content, with a good degree of difficulty, it's been foundational for my deep dive into AI products and have face to face conversations with Data and ML teams

Fantastic course as a starting point on Machine Learning Foundations, fully recommend for beginners, especially if you are not familiar on statistics or coding...

Great introduction to the concepts and I am glad it has the model building/training exercise at the end since it made the overall course much more meaningful.

A very clear introduction to the 'types' of Artificial Intelligence and other necessary concepts required in dealing with AI.

ein sehr guter Einstieg in die Welt der AI

I found the course for the most part to be interesting and beneficial in establishing a foundational knowledge of ML... however this does not come without some issues. The primary issue I had with this course is the peer reviewed assignment. In my view it should not be an assignment for this course which is clearly title and targeted for Product Managers. The assignment would be more applicable in a different course that branched off into developing the technical acumen of ML and not in what seems to be a foundational course for PM. This course is not written by PMs for PMs but rather by ML Engineers for ML/Computer Science Engineers looking to become PMs. Additionally a peer reviewed assignment in this context presents no real value as most people cannot provide SME valued feedback.

It is good course but in order to complete the final assignment the trainee needs to spend some time as a self learner.

I like how the course was structured, I just wished there were exercises in each week

This was a really useful course to get my head across the foundations of ML, and how it all works 'under the hood'. The final assignment really helps to connect the theory to the practical application of ML, however it throws you into the deep end on how to use the recommended tools. It's left up to you to figure out how to create and interpret the models with your chosen tools and as someone with a basic understanding of statistical modelling, I spent hours trying to understand how to do this. I think some additional modules that teach the student how to translate the theory to a practical tool would be really helpful.

I am writing this honest review as I am standing in front a cement wall of incomprehension and deception. I paid, I did all the modules, made the deadlines, and I passed all the quizzes with flying colors. But I am failing because this course teaches ABOUT different kinds of models and techniques. Not how to build a model. Yet here I am at the last 10% needed to pass the course and I am asked to build a ML model. It's like showing someone a built house in some details and telling them to go buy the tools, the materials and build a house. I simply don't have the resources and knowledge or experience needed. The course certainly didn't provide the necessary tools even after 6 weeks of work. Before I started, I specifically checked who this course was for, what prerequisites and experience was needed to pass and I was told no experience was necessary. Google's course on the other hand offers the same table des matières course but they list their prerequisites and prework necessary to actually complete their course. Turns out you actually need to know programming, be confortable with histograms, algebra and math equations etc etc Here is a star, half to be able to warn others and half for the beige and legit teacher of this course.

It is very upsetting that after this course you guys expect that someone with a very basic understanding of statistics and algebra will be able to carry out the exam. Waste of time!