Good KT

Good

nice

This foundational class is actually a pretty deep dive into the science/math of ML/AI. I wish there would've been guidance along the way of *how* to implement what was being learned (i.e., exercises to follow along with the instructor in setting up a model). Instead we just had to figure out to how to make things for the final project that we'd never seen done. Other than that, it was a good way to learn fundamentals.

This course helped me gain a solid understanding of machine learning concepts, especially around modeling. The lectures are presented lucidly and are very nicely organized and paced. The project assignment really forced me to apply what I had learnt in a real life scenario. Above all, it has triggered my interest to explore more apply what I have learnt in the role I am expected to play at work.

Liked the course, since it is a profound introduction to the broad field of machine learning. When it comes to NNs and CNNs, I think the course focuses too much on mathemathical aspects and lacks a bit the an easy and intuitive explanation.

Super useful course to familiarize yourself with the terminology and technical details of ML models. Not useful for learning how to manage the project, but super useful to understand the details of requirements to create a model.

Oversll, very good course. I learned a lot. Instructor is clear and knowledgeable. Te course materials are a bit biased to the ML side and the deep learning is a bit basic. The time assigned to the project is too short .

The training provides a good overview of ML concepts. At the same time pre-project data quality review and initial data analysis could have a more extensive coverage from my point of view

I thought the course had a good pace and was informative. I should have took advantage of the discussion forums more to ask some questions. Doing the project brought even more questions.

Not engaging. I'm sure the instructor is a technical genius however, I would suggest to refine teaching skills. All the courses are suitable for developers not Product Owners

good intro for machine learning, you will need to search and google lots of concepts to fully understand them so its gonna take more time to finish

It was a great learning. This course is perfectly curated for beginner who needs to understand the pros and cons of it.

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

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.

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.