The course is great. It does, however, need an update, as so much has happened over the past few years.

Well arranged course following a sequential learning path.

Very tough course. I don't think it's for beginner Level.

Great course. a few more real exercise would improve it!

Very good but also very technical. Refresh your math :-)

Well structured foundational course

I was eager to take this course to expand my knowledge of machine learning fundamentals and applications as a product manager. Overall, I found the course to be a valuable introduction to key machine learning concepts and algorithms. The instructor clearly has deep expertise in the field and I appreciated how he used real-world examples to illustrate the material. His lectures were engaging and he effectively conveyed complex topics in an accessible way. I also liked that the course provided opportunities to get hands-on experience through the final project. My main suggestion would be to consider expanding the curriculum to add more depth on supervised and unsupervised learning approaches. While I recognize the course aims for a broad overview, slightly more rigor would help differentiate it and better prepare students to apply these techniques. That said, I understand the challenges of balancing breadth and depth in a short course. The topics covered do provide a solid foundation to build upon. I particularly valued the practical guidance on evaluating and interpreting models - an area where product managers need to collaborate effectively with technical teams. To fully prepare product managers for applying machine learning, I believe the course would benefit from more in-depth coverage of supervised and unsupervised learning techniques. I posit that adding another course, or two that adds greater depth to all things Supervised, and Unsupervised learning in this course could make this course not just stand out, but also transform it to being the go-to course for anyone wanting to become an AI PM. Also, consider adding another module in the intro course to cover algorithms like support vector machines and Naive Bayes to make it more complete. Incorporating 5-6 hands-on guided projects using the methods covered would also let students get critical hands-on experience applying the concepts. With these enhancements, the course could become the definitive destination for aspiring AI PMs to build a strong foundation beyond surface-level ML literacy. That said, I appreciate the quality of instruction and see this as constructive feedback for an already valuable introductory course. Overall, I would absolutely recommend this course to anyone interested in gaining core ML literacy as a product manager. The instructor and content are excellent for an introductory survey course. I believe expanding on a few areas could make it an even more comprehensive offering and stellar resource for aspiring AI product managers. Please take my suggestions as constructive feedback to an already strong course. I appreciate the quality of instruction and look forward to learning more!

Lots of what, not enough why. Some lessons are just explanations of algorithms without examples of why or how they would be useful. I can look up these concepts anywhere, wikipedia, google. I'm taking this course to learn why something is important, not to go through motions like a machine.

A completely new experience, this course, a lot is explained in the video, but then solving a complex task without practice, where you have to repeat everything, shocked me at first. Hope you understand this english better, like me.

I am indifferent to just reading the slides vs going through the slides. The quiz and slides are great not the content delivery.

The instructor was a bit boring. Talked a lot about equations but never showed examples of how to use them.

Please add some videos about tools such as excel and how to use this models in that

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!

difficult to follow for non coders. The language is very very technical. Waste of money.