great experience and learning lots of technique to apply on real world data, and get important and insightful information from raw data. motivated to proceed further in this domain and course as well.
Very well structured course, and very interesting too! Has made me want to pursue a career in machine learning. I originally just wanted to learn to program, without true goal, now I have one thanks!!
By Tsz W K•
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.
By Guenael S•
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.
By César R P•
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.
By Anad K•
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.
By Matt C•
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.
By Abhi B•
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.
By Ankur C•
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.
By Zhu L•
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.
By Refik E•
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.
By Tony K•
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!)
By krishna c•
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
By SHAILESH K•
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.
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.
By Kedar J•
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.
By Mario H•
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 :-)
By Mohammad M T•
I think there were some small problems in the assignments and quizes but all in all those problems made this course assignments even more powerful because it demanded more effort to answer those questions properly.
Totally if you want to get a good sense of machine learning and step into AI , this course will not only give you basics and principals but also you will be able to build and understand different models using python.
By Erick S G P•
All the exercises were very challenging and allowed me to apply all the knowledge acquired during the lectures and even more. I loved the fact one has to search for extra information for doing the exercises, because that pushes me forward to learn to search in other sources. Also loved the freedom that there is when solving the final assignment. That is the best expression of a real world challenge and allowed to exploit my creativity.
By Illia K•
This course gave me some tools to use in real life. It's pretty abridged in time because they are trying to cover a very big topic in only 4 weeks. It won't give you a comprehensive set of knowleadges, but a good basis to proceed by yourself. Also some basic knowledges are reqired in computational mathematics, statistics and programming for applying this course. I highly recommend this course as a first step into machine learning.
By Ammar A M•
One of the best ML courses on the platform. I highly recommend it to all data-science enthusiasts. It would be nice to have pandas data-wrangling skills before tackling the final project as it is a must. Totally enjoyed the final project! was a great learning experience seeing my classifier AUC going from 57 all the way to more than 76 and the impact of feature importance and cleaning on the model performance was eye-opener!
By Michael T B•
Great class! I had fun learning many new things in this course. The professor did a very good job at taking a complex subject and making it simple and easy to understand. The code and assignments were straightforward and not overly difficult. The real quizzes/tests in this course were appreciated as this felt more like a "real class" where one can really learn a lot. One of the best online classes that I have taken.
By Parvathy S•
Very useful and true to the name, it teaches Applied Machine Learning - how and when to carry out the various algorithms on a dataset, how to tweak the parameters and tune the model. Really Really helpful if you're looking to finally get your hands dirty on data after reading all that theory!
Also gives brief but necessary summary to all the different algorithms with intro to deep learning as well. Highly recommended!
By Benjamin S•
I thought this was a very good course in Machine Learning using Python. I took Andrew Ng's Machine Learning course before this one, which I would highly recommend! I enjoyed this course because it taught me about scikit-learn, which I plan to use in my career. I also purchased the recommended textbook "Introduction to Machine Learning with Python" from O'Reilly, which I found to be a very useful reference.
By Fabio C•
The course is well done and both the lectures and the practical assignments have generally a high quality. If you come from a theoretical background, be aware that this is a very "high level" course, meaning that a lot of attention is put on the practical application of the different ML methods (using the sci-kit learn library in python), but very little is said about their mathematical foundations.
By Zhuohan X•
All complicated math acknowledges were cut off and fully focused on applying ML using python. As an energy engineering master student who doesn't have much programming experience, I find this course very useful. PS. I've previously taken the specialization 'Python for Everybody' to get familiar with python. I suggest doing the same if you also have no idea of python just like I did when I started.
By Perry R•
Excellent instruction and challenging assignments! Sophie from the teaching staff was very helpful and responsive to forum posts. Thanks to Kevyn Collins-Thompson for a great survey course in machine learning. The only downside was that the auto grader has limitations which inhibited some exploration (one can not keep plots in the submission is an example), but I'm sure that will get worked out.