This course is very hands-on. It clearly teaches Machine learning beyond python notebook. I enjoyed this course and currently taking the second part of this specialization "Machine Learning Data Lifecycle in Production". Great content from Andrew Ng and Robert Crowe.

This course is full of practical insights on how to tackle real-world machine learning problems. Specifically for me, I was working on a problem for a long time and after taking this course and applying the skills I learned, I was able to get a wonderful outcome.

It's really refreshing to see the "behind the scenes" perspective on ML algorithm development. Although I have been working in Computer Vision for more than 10 years, I found Andrew's frameworks to think about the project lifecycle and data very useful. Thank you!

This is really one of the most important courses I ever took! Andrew as always explaining in a very clear and interesting way. The course material are so useful, I have been working in the ML field for 2 years and I learned a lot of new concepts in this course.

Great theorical material to understand ML projects. The 1st (ungraded) lab exercise was not very clear though when playing with the front end and back end application, it would be nice to provide more information or tips on how to complete it

Great course, concise but valuable insights on how ML is actually used in the real world and what problems we typically face when deploying ML to solve actual business problems. Looking forward to the upcoming courses in this specialization.

It covers a lot of the real world problems data scientists find when trying to build machine learning solutions. Many of the best practices reviewed here are a common sense thing but having it wrapped toghteter here was really great !

Deep learning courses are always best, cover all aspects in theoretical as well as more emphasize on practical knowledge which helps a learner ready for the real life challenges in Data science domain...Thank You Andrew NG and Team

This course gives useful insights about deploying machine learning systems in production from PoC stage. These insights are the same that an experienced ML engineer would have got in his/her practical experience in the industry.

Amazing introductory course that gives you the full scope immediately, as well as many theoretical details on each section. I expect the following courses of the specialization to dive into more technical things and frameworks.

Excellent course!! A new way to understand the key factors to master the Machine Learning lifecycle. This is much more than one course, this is an invitation to change our mindset through an exciting journey with Andrew Ng!!

For someone not starting out with machine learning in production it is a good introduction and for someone with experience it can be good with another perspective on ml in production, just run the videos on 2x speed.

I needed to reset my deadline and start my learning again from week1 but it say error and was unable to reset the deadline. Not happy with the support.

this is not about the course content but support for new users.

The course was fairly compact and you would be able to finish each week lesson every day if you eager to do so. It was fun and educational. I loved the surprise in the last question of the optional quiz in week 3.

Great course explaining concepts on ML lifecycle and deployment, especially touching topics like concept and model drift, monitoring models, error analysis, experiment tracking, pipeline and lineage. I loved it.

This is a great introduction for how the mature machine learning product could be morph into mature products with multiple challenges. It helps me a lot for understanding how future AI industry looks like !

This course would be very useful if you are ML-engineers, data scientists. However, this course does not teach you how to code. To code, you need to take Deep Learning specialization or some other courses.

Very practical materials and application-focused methodology! A lot of rule-of-thumb gathered from ML pipeline experiences. Clear definition on acronyms and mainly easy-to-follow non-technical guidances.

Very valuable course for those who already have some knowledge on machine learning or AI applications. Very close to what systems engineering processes recommend, as when seeking ISO15288 compliance.

Excellent course. Happy to reconnect with Andrew after a few years to continue my ML journey.

Full of useful tips & tricks, but also deep thoughts on how the ML community should evolve its practices.

Excellent course, as always. Very well explain for both Data Sicientist, Software engineer and Manager (with some basics undertsanding of ML). One of these courses that Data Sientist should follow.

really a great course. It'll really change your way of thinking ML in production use and will help you better understand how can you leverage the power of ML in a way that I'll really create a value

Introduces you to the basics of MLOps in a well paced mannar. Would request to add more examples of structured data sets, as many companies usually are dealing with the related problems.

I really enjoy participating in a great class like Andrew's class. It's full of useful and applicable points that I encounter during a real prj.

Thanks for sharing this asset with us :))

The content of this course has been especially useful for me. I wish there were more emphasis on the tools recommendation as well, but the theoretical knowledge was just fine. Thank you!