This is not a good course for beginners.

no teacher intraction

Dude

A great introduction to the practical side of machine learning, particularly if you have already taken Andrew Ng's course. It covers a *lot* of material and the pacing is *very* fast. Week 2 is particularly long, and if you are still a student/working it may take an extra week to complete the course. Quizzes and assignments are not terribly difficult, but be careful of the project assignment in Week 4 (though the bar for a 100% is quite low!). Finally, the accompanying Jupyter Notebooks are very helpful and there are many helpful links to outside resources as well.

A few of the lecture videos feel like an early draft rather than production-quality, with lots of time spent on repeating phrases. The instructor mentions things to be covered "later," but that "later" never comes (for example, in discussing Grid Search). For some background, this course appears to have been repeatedly delayed before its release. To me, is understandable that the creators wanted to get this course out given the demand, but the rush is felt.

Ultimately, however, this is still an excellent introduction to Python Machine Learning, and I do feel the course is well worth taking. Just be prepared to do some more individual learning; however, shouldn't one always be for an online class?)

Muy agradecido, mis felicitaciones al Profesor Collins-Thompson, se muestra como una persona amable, dinámica y con alto grado de conocimiento, gracias a sus enseñanzas estoy aprendiendo más sobre el proceso de machine learning, siento que aun me falta mucho por recorrer, sin embargo, a lo largo de este curso aprendí los métodos, tipos de modelos, herramientas tanto para clasificación como regresión enfocándome en el área. De igual forma la literatura es muy interesante, se encuentran artículos que al leerlos vas comprendiendo como ha sido el proceso de transformación en este campo y gracias a esto, se me han ocurrido ideas que me gustaría compartir o estructurar para evidenciarlas de manera mas formal.

Muchas gracias por el apoyo, gracias por las observaciones y anotaciones dentro de los foros de discusión, siento que puedo seguir aprendiendo mas y es por eso que estoy agradecido por mis conocimientos adquiridos, los cuales siempre puedo retroalimentar viendo el curso nuevamente cada vez que lo considere pertinente.

5 starts for content. The lecturer and slides were good. The assignments were often difficult and took many hours longer than the stated 3-4 hours. Assignment 4 was particularly heavy in time. I finished the course feeling equipped and confident enough to take on straightforward machine learning projects from start to finish. I've dropped a star because the autograder uses an older version of Python and older libraries, which meant I had to spend around 8 hours re-engineering my *correct* code to conform to old libraries.

Addendum: I've uprated the course to 5 stars after having just completed the fifth, optional week on unsupervised learning. It's unassessed but does give a nice introduction to the subject. Thanks!

Though I would have liked a bit more insight into the actual algorithms behind machine learning, this class did a great job of giving us problems and forcing us to be resourceful and hunt down the answers, whether via course forums, Stack Overflow or other random Googling. We were exposed to a ton of different algorithms and libraries, and we got to experience the whole spectrum of data science: data importing, cleaning, exploratory analysis, feature selection, model selection, parameter tweaking and even some visualization. It was a lot of fun: challenging at times, but oh so rewarding in the end!

Very nice class for people who have some intermediate knowledge in Python and who want to dig in, or consolidate their knowledge in Machine Learning. Great overview over scikit-learn, also going into details, and I also appreciated the part of the class about model evaluation. First week might seem not overly difficult, but the intensity of the class ramps up significantly in week 2. For me the level was challenging enough, without being overwhelming. I enjoyed taking this class and obtaining my certification at the end was a very nice reward. A big thank you to University of Michigan.

This course is excellent. It contains a great deal of instruction each week (1-2 hours), and it also has many supplemental references for people who want to go deeper. The quizzes are actually very challenging, and require study of the material. The assignments were easier for me than the other courses in this specialization, but they were focused on application of the material to real world problem, which is the purpose of the course. The final assignment is very instructive and challenging. The instructor is very knowledgeable, and teaches in a thorough, but easy to follow, manner.

The course was great. The final assignment was especially useful as it was almost completely unguided and gave us a dataset which is unlike the tidied up, dummy datasets you'd find in online courses. So it was, by some means, an independent project. The data was messy, full of errors, and maybe downright ugly. We needed to clean the data and do quite a bit of preprocessing to get it in a shape suitable for fitting a machine learning model. The project gave a taste of how a real-world machine learning project might be taken on. Thank you very much, Professor Collins-Thompson!

I completed the Machine Learning Specialization Certificate before taking this course. This course is an excellent applied course that quickly gets into the key aspects of using sklearn. This course is ideal for both new learners and experienced learners who just want to learn more/revise about machine learning. For the final assignment, it requires substantial data cleaning techniques covered in Course 1 in this specialisation. Overall, I feel very comfortable with using Python for any reasonable size of machine learning problems after taking this course.

The class provides a perfect introduction to the scikit-learn Python module. The videos are engaging and insightful. The quizzes are challenging while not requiring too much time writing out solutions (it does take time finding some of the more subtle answers, by reviewing details in the videos). The executable modules are perfect to bootstrap machine learning projects. Homework assignments can get complicated, and you should be familiar with advanced data structure manipulation in pandas and numpy to make progress. Assignment grading is very well done.

Great course on the basics of machine learning. I'd say this course is a great dive into sklearn, which is actually great for many purposes. It barely covers Neural networks, which are the hot topic right now, but it gives you a lot of tools that will suffice in the vast majority of cases, and teaches fundamentals that are also applied to deep learning if one decides to go forward and learn other libraries like tensorflow. All in all, a great addition to anyone's toolbelt, be it engineers, scientists or people trying to jump to a data science career.

Excellent course for Machine Leaning. Discusses wide range of Supervised machine learning and gives a very brief introduction on Clustering algorithms(Unsupervised). Users can immediately put to use the knowledge gained during the course.

Some more briefing about feature transformation and other such elements can be included in the course material to make it better. Also unsupervised machine learning could have been included with grater depth. Overall this course is highly recommended to aspirants interested in ML with some python knowledge.

The course was very well prepared and the instructor presented the material clearly and informatively. I've seen some courses where you spend more time trying to understand and keep up with the instructor. In this instance, this was not the case and you could spend more time understanding the material. The instructor spoke slowly and clearly.

I do have to say I purchased the corresponding book as recommended but I didn't feel it was necessary. Good book, I just think the material in the course was presented well enough on its own.

The course provides a good overview of ML techniques and potential gotchas, and then goes into a real life example which helps round up the theoretical overview with application to real world data and their challenges. This provides a great introduction to ML which positions you to delve into it in much more detail and help in your journey as a Data Science practitioner. Must commend University of Michigan on coming up with the fine balance of theory and practice, which is essential in this rapidly changing space.

Great course for Machine Learning Algos. This series of lectures also helped me in understanding two beginners books for ML -

1. Introduction to Machine Learning

2. Hands on to Machine Learning.

Professor taught in a very informative and easy to understand way. Really thankful to the professor. Each and every algo is well explained with strengths, weaknesses.

questions in Quiz are very good these were not so easy and not so tough.

I will recommend this course if you want to learn ML using Python.

Thanks a lot, sir.

It is such an interesting and practical course for machine learning. If you are looking for courses which allow you to apply what have you learned in practical problems, this is a very good option to consider. I liked how this course is structured, it teaches you the theory first, and then ask you to use what you have just learned (of course, not 100% coverage), which definitely provides a valuable learning experience. Highly recommended for someone who is interested in data science in general.

The course is very well-designed, with the first three weeks learning basic know-hows of all the tools we need, and the fourth week make full use of every model we've learned.

Even people with no prior CS background can get along well enough.

Getting 100/100 out of the final problem is actually a passing grade, very easy if you use what you've learned so far the right way.

When you're willing to spend more time exploring the models, methods and parameters, the reward will be worth your efforts.

I thank Dr. Kevyn Collins-Thompson and Coursera team for the excellent course. I have learned valuable skills from the course. Dr. Thompson explained ML concepts very skillfully and made the course fun to follow. Assignments are very well selected and reinforce the class concepts. Over-all the course encourages learner to investigate and apply different ways to do same task. I recommend this course to those who are willing to learn machine learning and can't decide where to start.

A solid course. The help found in the forums was also way more useful than the first course in this series. While course two was generically useful, this third course was technically useful. A very good introduction into sklearn. The video instructor/professor was also very clear and methodical in presentation. The assistance by the class monitors was leaps and bounds more useful in this course than course one (I almost quit after course one because of it, so glad I didn't!)

excellent course for following reasons:

1. Excellent i python note books. What ever a student must know is kept in it.

2. every topic is explained simply and well upto what ever we need to know.

3. if you are not in academic field(not planning to do phd on this stuff). Trust me how ever advanced courses you do but after a week or month. these are the points which one need to remember.

4. Course and programming labs are in perfect sync.

Thank you very much for keeping this course

Great intro course to Machine Learning. Gives you a good overview of the main models and Python needed to code. I liked the fact that it did not get too detailed into the Math foundations of ML. There are other courses for that.

I can apply what I have learnt right away on my job.

Highly recommend.

One Note: this course is over 2 years old and the Staff is pretty slow to respond. But the Forums have enough information to get you to self-solve your problem.

Good luck.

Great course filled with a lot of details. The course does a great job in teaching all the important concepts. I felt the feature engineering should have been a dedicated topic. I got a lot of hints from the discussion forum and surprisingly there are even more concepts you have to learn for building a pipeline, treating categorical and numeric features differently. Overall challenging week4 assignment gives you confidence to deal with real world problem.

I have done several of Coursera Courses and also from Udacity (Deep Learning Nanodegree) and I find the courses from the University of Michigan really good. This one for Machine Learning is really specialized for the Application of Machine Learning Algorithms. Sometimes a little too superficial, but it is enough for start working with Machine Learning. The Test at the end of the week are a little difficult but you learn alot from them :-)