Much more detailed than the previous two courses. The lecturer teaches with more verbose slides and thus gives you a more detailed overview than the lecturer in the first two courses in this specialisation. The assignments are much easier as well. But still thoroughly useful and I have to admit a welcome break from the gruelling process that typified the first two courses!

The course allows you to play with a multitude of supervised (and unsupervised - optional) machine learning methods. Professor Collins-Thompson is very clear and knows perfectly well how to convey concepts and how they should be applied in real situations. I recommend it to anyone like me who needs to be able to develop simple codes and understand what they are doing.

Very good course for a wide spectrum of audience interested in Machine Learning. I just had a basic learning of ML and Python, but the course was structured so well that I could catch-up. Also offers an interesting peak into Neural Networks and Deep learning. Overall, an excellent course with clear and attainable objectives, backed by high quality content and data.

This is great course with very practical methods to sovle real problems in various fields. I think there should be a additional course regarding Deep learning, which I think would be very successful as well.

Moreover, this course can be combined with Andrew`s ML so that we can have both theoritical concepts and practical experience of Machine Learning in python.

This course gives you a very good knowledge how to apply machine learning techniques (mostly supervised learning) and basic things, like how to preprocess the data and what are the pros and cons of various models and which models to be used based on the kind of data given, and many more basics which are required for a deeper understanding of Machine Learning

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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.

I've never learned this much in 4 weeks. I studied more than 4 hours a day to keep up with so much new information.

Videos are great and professor Collins-Thomson does an amazing job teaching these courses.

Althogh assignments are extremely hard and a little unbalanced, I belive this is one the best courses I've ever had.

thank you so much.

Giving a solid understanding of Machine Learning in Python by utilizing the scikit-learn library. Although, there are some limitation due to the online platform, the 'Discussion Forums' really helps in those problem. Overall, I enjoyed enrolling this class. Looking forward for any new classes which dive deeper in Applied Machine Learning

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

It was great learning experience.This course exposed me to various parameters of machine learning using python programming and helped me to gather knowledge about the significant use of Pyhton Programming in the field of machine learnig.Pandas,Regression topics are rightly and deeply understood to me because of this course.

THANKYOU!!!

This Course gives a very good understrandig of the solid basics of Machine Learning that anyone looking to touch basis on this topic for a career progression would find very beneficial. It describes the core concepts in the abstract level that is needed to know as a kickstart, providing a great optional material and community forum.

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.

It is a wonderful course that convers a basics of Machine Learning, the instructor provides an excellent explanation of the topics and the Jupyter Notebooks helps you understand and document all the concepts learned through the course. If you want to build some knowledge in ML this is a good option to start this great journey!

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 applied course, while still teaching you the basic concepts. You can start using machine learning solutions to your problems right away with confidence. The course covers a lot of ground, so expect some topics to be treated rather superficially. It provides a lot of material if you want to expand your knowledge though.

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