The content (slides, python scripts) is very structured. The lecturer explained very clearly. The reference articles were super inspiring. Also, the assignment is very well designed and relevant to what's covered (in comparison, some other courses might have very difficult assignments which need much more self-learning and cause frustration). Thank you!!

Excellent course, easy to understand, useful and enjoyable to do! Two minor comments: it took me a longer than the estimated times to complete the Quizzes; I have Python programming proficiency and a small amount of background in Machine Learning. I would have preferred the final assessment to have an extension to it which required a more advanced model.

Great course!! And this field of science/technology is fascinating.

The only comment that I would do is that it might have been useful to include a whole pipeline on the creation of a simple machine learning software from the data collection to the end result. I guess that is the goal of the next course on text processing, so I'm looking forward to it.

Excellent course!

Machine Learning is today a buzzword and you do not really know what it is until you do it. The University of Michigan has put together a great program that takes you from the basics of Python to the latest Machine Learning techniques.

I started without knowing Python, and well, I cannot say that it has always been easy, but I DID IT!

Thank you all for such an awesome series of courses.

I find these courses really challenging, especially the final assignment. But it is rewarding too, coz you feel, that you CAN solve such tasks in real life too.

Thank you Michigan team for such efforts. During the last 1.5 years I managed to progress from 0 programming knowledge to solving ML tasks

It was a good introduction to machine learning. The assignments and quizzes were well designed to encourage self-learning, which in my opinion is one of the most valuable skills an aspiring data scientist could learn. All in all I am very satisfied with the course and I look forward to enrolling in the other courses in the specialization.

This is a very nice course in Applied Machine Learning. For getting the most out of it, it would be nice to have taken ML Specialization from Andrew Ng which will take a deep divce into the working of ML models or have good amount of knowledge in ML. Having familiar with ML concepts, you would find this course really useful.

Regards,

Binil

This is the most useful machine learning course in the internet. It helped me to understand machine learning algorithms very well that I never saw in other courses. This course covers most of the machine learning algorithms that needed nowadays. Thanks to Michigan University and Coursera to make this course to be available online.

Highly recommended. Great practical overview of machine learning approaches.One shouldn't expect the underlying implementations from this course due to the time strain - only a few weeks, and should take Andrew Ng's machine learning class for that.To go even deeper for some methods, one should take more machine learning classes.

Very interesting and engaging course. I liked graphical comparisons of different models and their params. Module notebooks were very handy while doing assignments. All homeworks were not trivial, developing and demand attention to detail. Big plus for teachers posts at forum - they help a lot while doing quizzes and assignments.

An excellent overview of current machine learning knowledge and practices. This course is very information dense and requires additional reading and time for the assignments. It is challenging for an 'intermediate' level course. Some prior knowledge of machine learning is recommended, and strong Python skills are required.

Very good course for either an introduction to machine learning or to refresh old skills. It's also very good at putting emphasis on topics that data scientists may overlook / not pay much attention too, so having this as a reminder to look deeply into each algorithm and its application or limitations is incredibly helpful.

excellent, practical introduction to (mainly) supervised machine learning in scikit learn. Next to Python specific handling of models, also conceptual issues like parameter tuning, feature pre-processing and - very nicely - data leakage are explained. examples can get tricky without solid grasp of numpy and pandas packages

this is an interesting machine learning course

can quickly understand the basic idea of machine learning and know how to build different models in python and select models based on different standards

it is a very good course to start with machine learning and can arouse the interests of learning more in this emerging field

The course covers a many topics of the ML world.

The exposition of the arguments is well organized.

The assignaments and quizzes are difficult enough to force you to really understand the lessons and learn the arguments but are not impossible to be accomplished.

The teacher are always ready to help you in the course forum.

this course may be the most challenging one I have ever met, those concepts and examples I have never thought would met in my life. but after intense learning and excellent course arrangement, I may get a little sense of machine learning now.

Thanks for the great job, dear applied machine learning in Python team!

I did this course only from the entire specialization so it was a little hard to catch up but the difficulty made me even more excited to keep going and finish every bit of the course. I really appreciate the amount and quality of content, quizzes and assignments. Totally worth my time. Thanks UoM and Coursera!

Lots of material to cover in this course. From supervised learning to the optional un-supervised learning schemes. A good introductory course to all theory there is to know on applied machine learning. The professor gives a glimpse of internal mathematics too. Interesting course, but lot of material to cover.

An ambitious but systematic overview of a wide range of machine learning techniques using scikit-learn and other Python libraries. Prof. Collins-Thompson is a steady and clear explainer of somewhat complex topics. The exercises and quizzes can be challenging, but are very worthwhile.

Overall, very well done.

Good introduction into the scikit learn package, took way more time than advertised but I also learned more than expected.I contrast to course 1, the assignments were easier, but the quizes were harder. Distribution of materials could have been better: week 2 has by far the most material to digest and learn.

I have been confidently guided through the complexities of Machine Learning through perfect mix of lectures and reading materials. Quizes and programming assignments served as very helpful tool to zoom in on specific details which in further assignments will make the difference between success and failure.

Brief but in-depth introduction to many modeling methods and using them in python. It provides a great foundation for the rest of the courses in this specialization, but I wish other courses would be developed in collaboration with this intro course, rather than a series of independently designed courses.

Very interesting and informative! The material outlined in the course, difficult to understand, IMHO, but the organizers and the teacher managed to present it in an accessible form. Special thanks to Kevyn Collins-Thompson for his lectures and Sophie Grenier for her work and attention to the forum.

Coursera has made possible for millions of students worldwide to access the best quality of education through their medium. An opportunity to learn and develop as an individual changes a person's life substantially and most importantly Coursera is providing this opportunity to millions for free.

Lectures were good, direct, not too complicated to understand and conceptual. I suggest though that maybe visuals on the lectures could be even better. Assignments were reasonably difficult and forums were great to review the problems and find solutions for the errors. Thank you for this course