I thoroughly enjoyed this introductory course to ML. It was a intensive introduction to various models and techniques.

well structured, ambitious content, applicability and mathematical methods must be thought through carefully

I think the information should be better organised. Meaning, it should follow a more linear progression.

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

Solid introduction. It would have been great if we could have gotten into using Vertex AI.

Found some of the content a bit hard to follow, some more real world examples would help.

Well arranged course following a sequential learning path.

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

Awesome course - great learning of the foundations of ML

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

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

we may need more engagement for AI model assessments

Well structured foundational course

excellent

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!

Overall, I found the course valuable and I do think it adds meaningful insight into machine learning concepts. That said, based on my own experience, I would strongly encourage anyone interested in taking it to make sure they have the prerequisite knowledge first. There is a fair amount of higher-level math and terminology that the course moves through quickly, and having that foundation already absorbed would make the learning experience much smoother

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.

Was trying to do too many things in a single course. Could have done with introduction of less concepts - I am not sure how many people will remember K-means clustering 1 month after doing this course.

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 need a lot more doing to learn this type of thing being a non technical person

The title is deceptive. It's 90% data analytics which I do understand is the foundation of ML but the rest 10% is not worth the value and not very well explained. I'd rather do fast ai free lessons 4,5 than pay for this.