Applied Machine Learning in Python

Learned a lot from this course.

I was excited going into this course because the others in the series were taught well and I had learned a lot.

Learned a lot in this course!

to give the learner a good over view of how to apply ML theories into action very good This is a great course I learned a lot, especially it familiarize me with the SKlearn toolkit which is very very handy.

I've learned a lot.

Awesome.I learned a lot of fundamentals machine learning.

Learned a lot very excellent course, must take if you are welling to deal with data and applying ML al.

Learned a lot both about the concepts and how to apply the methods using scikit-learn.

I learned a lot from this course, but I do not feel like I truly understand everything.

I learned A LOT in this course and was pretty proud to pass all the assignments.

I learned a lot and I am already leveraging what I learned in the course at work.

Learned a lot from it.

Well presented and good organized python notebooks, quiz and assignments.Enjoyed the project very much.Looking forward for future classes this is an interesting machine learning coursecan quickly understand the basic idea of machine learning and know how to build different models in python and select models based on different standardsit is a very good course to start with machine learning and can arouse the interests of learning more in this emerging field It is a very practical course if you have learned the Andrew Ng's Machine Learning course.

Combined with Andrew Ng`s course on Machine Learning it`s great foundation for futher development as AI specialist.

Perfect and hard course than Andrew Ng's ML course!

I took Andrew Ng's Machine Learning course before this one, which I would highly recommend!

It's a perfect checkpoint after Andrew Ng's machine learning courses, by making experimental practices over theoric practices.

An excellent complement of Andrew Ng's course on 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.

A great introduction to the practical side of machine learning, particularly if you have already taken Andrew Ng's course.

I will recommend Andrew Ng ML course to do as well because it covers too many things than this module.

(4.5/5 is the rating I would give) excellent course Really well explained theory without too much of a mathematical deep dive that provides a perfect set up to learn about machine learning from a purely math/stats perspective through Andrew Ng's Machine Learning course or self study Great course!

This is a very good course about How to apply Machine Learning but I think before taking this course the student should take the Andrew Ng machine learning course by Stanford University to Learn the Important Mathematics behind the ML algorithmsBut Enjoyed this course a lotthank you It is definitely the best-organized, best-paced, most-worked-on course in this specialization, and from the MOOCs I have ever taken.

Before doing this course I recommend something like the course of Andrew Ng (without that one, for me it would have been more difficult to follow this one).

This course will teach you the application of machine learning in python.

It help me to understand details of Machine Learning in Python.

Unfortunately, for me, this course (not the specialization) seems to be a "review of" Applied Machine Learning in Python" rather than "teaching" Applied Machine Learning in Python.

Excellent material for intro to ML An excellent overview of Machine Learning in Python.

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.

Overall a very good course for getting hands-on machine learning in python.

Not very deep, but definitively very wide and appropriate for an overview course of machine learning in python.

awesome DS Great course for beginners to start with Machine Learning in python.

Great intro to the tools of machine learning in Python Very informative, useful practice Tough class, learned not to give up and keep trying.

Great professor with lot of real world experience.

Very good weekly assignments allow students to well consolidate course contents on real world practices.

I mean if the goal is to train our to do some real world data you may can shrink the dataset, the large dataset would takes more time to training which would cost more time to debug.

A course that gives not only solid understanding of Machine Learning, but provides with skills to actually practice it on real world datasets.

I am unenrolling because of the following reasons: 1) instructor lead training is very very boring - the gentleman keeps talking in same pitch and there is no lucid explanation behind the math that is constantly thrown at you2) the course does not bother to put in any real world scenarios to correlate the content withOverall really poor experience The course is fantastic Concepts were clearly taught and helped me gain knowledge in techniques used in machine learning.

After taking this course I feel more confident in my ability to work on real world machine learning tasks.

Learned a lot and have used a bit in the real world!

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.

Overall challenging week4 assignment gives you confidence to deal with real world problem.

Since this course is mainly focused on the ways to use the machine learning skills in the real world problems, if you are interested in the mathematical approach of each skill, you might need to look into the other courses.

At the end of this course I feel confident that I can *actually* apply machine learning to real world problems and competitions.

It'll familiarize you with different models, evaluation metrics and basics of machine learning and let you practice with some of the real world datasets during assignment.

Very usefull, easy to understand and full of examples.

Very easy to understand This is the most useful machine learning course in the internet.

Hands on to Machine Learning.Professor taught in a very informative and easy to understand way.

Professor Kevyn Collins Thompson, explains the topics with examples in python which makes content easy to understand.

It focuses on ML application and it's easy to understand.

The professor did a very good job at taking a complex subject and making it simple and easy to understand.

nice course and easy to understand Excellent overview of many ML algorithms.

I am just about to begins my Module 2 but I have realized that how much easy to understand and to the point course is.

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.

Very helpful discussion forum.

However, I thought the lectures in particular were needlessly more abstract than the previous data science courses in this specialization.

The earlier data science courses were great because you could test code with the lecturer as the video progressed and learn from it.

Good resources for preparation for technical data science interview!

A good stepping stone towards a career in data science.

The Applied Data Science with Python specialization continues to deliver with Applied Machine Learning.

Professional data science projects will not use notebooks but script files instead.

Great content Great collection of applied Data Science concepts, worked examples and challenges using python The course is a great overview of the basic algorithms that every machine learning practitioner should know.

A very good course to start journey on data science.

The lecture content here structured to discuss broader machine learning concepts, rather than setup to follow along in the notebook.

Autograder is not too verbose and as a result I spent some time struggling with debugging the code rather than figuring out how to solve machine learning related problems.

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 video lectures are very much to the point and this course is especially useful for someone who is more interested in application of Ml algorithms rather than their development.

The gap between the lecture content and the assignments is the typical chasm for this U.Michigan "speciality", and frankly you end up basing assignment answers more on internet research rather than lecture content.I'd sum it up as a substantial missed opportunity.

It's more about memorizing a lot of concepts rather than understanding them.

The course is well balanced but the progression becomes quite agressive at Week3 and culminate at Week4 with a real life case assignment without much guidance.

Not enough real life examples throughout the video, makes it very hard to concentrate during the whole lecture.

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.

Well structured course that gave a good insight on applying Machine learning to real life cases.

Should have provided more mathematical theory in the resources section.Assignments should be a lot tougher and on real life data sets which require recodings and transformations.

It would be better if they used Christopher Brooks from the first two courses as he is more engaging and he seems to have a lot more experience in public talking.

better than the first two courses of this specialization for the content is coherent and the assignment is relevant to the knowledge taught in the course video.

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.

But still thoroughly useful and I have to admit a welcome break from the gruelling process that typified the first two courses!

I have rely enjoyed this course because it is very informative The course material and Professor Kevyn Collins-Thompson is awesome.

The course is excellent and Professor Kevyn Collins-Thompson goes to the lengths and breaths to explain various machine learning algorithms and also provides a hands-on the syntaxes for the code to provide a deeper intuition to the problem.

very good course for intermediate level learners .learned a lot in such a short time.thanks to prof.Kevyn Collins-Thompson.

Kevyn Collins-Thompson is a legend This is a great course for those with limited experience of machine learning, wishing to quickly grasp how to apply machine learning methods and get their hands dirty.

Excellent pedagogy from professor Kevyn Collins-Thompson.

Very Good A good introduction to Scikit learn How can i pass without reading discuss about problem with notebook Excellent Course!

excellent, practical introduction to (mainly) supervised machine learning in scikit learn.

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.