Very enjoyable and generally quite understandable introduction to machine learnings with hands-on approach through the course project. It was a bit too fast-paced and generic for my liking, but many options were offered and highlighted for finding additional learning documents and courses to be able to deepen the knowledge acquired in this course.

As the title states, very practical way to show you how this is done in R.

Most of them are lines of codes and some explanation. There are tons of details behind that and remains un-explained.

As other courses in the specialization, students need to do a lot of self-study to further understand machine learning.

But at least, learned a lot.

I think some material might need to be revised, but I thought it was very interesting to see everybody's model building code (and perhaps that can also help me in the future).

While it is mixed with other notes, I have more detailed thoughts in this blog post: http://cdwscience.blogspot.com/2019/12/experiences-with-on-line-courses.html

The course rushes over a lot of concepts and it already shows its age - however, it's a pretty solid introduction to machine learning from a practical perspective. It will provide you with a lot of ideas for further investigation and exploration and in the end you'll end up with a wide vision of the machine learning process.

The lectures were great and engaging. I felt like they went too fast. Jeff says at the beginning that this is just an overview and points to some other resources. As an overview, this class works well. You can expect to learn a bit about what machine learning is and how to to do it using the caret package in R.

A reference solution for the quiz questions as there are in some other courses in this specialization would have been nice, since I got sometimes very different results using the newest versions of the libraries and I'd really like to know, if I made any big mistakes and it's not only because of my setup.

The lectures are very good to get the basic knowledge about machine learning. One suggestion is that the lectures can be longer, covering more detailed stuff and a little bit more advanced materials. Moreover, some codes are not explained clean and clear for me. Hope it would be better in the future.

The lecture material is great, but the quiz material is in need of updating. R and it's packages have gone through many updates since the problems were written so it is sometimes difficult to reproduce their results even with running the sample codes given after getting the answer correct.

This course allows you to implement practical solutions using machine learning algorithms without having to know the mechanisms behind the calculations in detail. Unfortunately questions in the discussion forum were quite rare and many questions were not resolved during this course.

The course covers few important topics in R like cross validation, decision trees, random forest etc. which comes in very handy for a data science aspirant. It expects the participant to have a descent knowledge in R. Overall, I am pretty satisfied with this course. Thanks!

This course is brief but it has the 2 best ingredients for having a really decent first step in Machine Learning:

1) It covers a broad group of different algorithms

2) It provides reference material for those in which you want to get deeper.

Really good job in this course.

I liked the course and found it informative, but wish there were more stuff on unsupervised learning neural network algorithms (SOMs). Learning about most used algos are great, but would also like to know other machine learning algos that are used concurrently.

Great course! The hands-on approach make it very useful for one to start doing some very interesting analysis in real life! Thanks a lot! You guys could only make some efforts in updating some classes and packages used in quizzes. But the rest was great!

Lectures were not very detailed.

Quizzes were good and challenging, but too many times the results didn't match the answers even when the random seed was set right

Final project should have been more challenging with more models to build and compare

Very good course. The content can be enriched with some more technical details behind the various techniques. There needs to be 1 more course on Practical Machine Learning in the specialization as 1 course is far too less for such a vast topic.

Good course to get only the basics of machine learning. The assignments and quizzes are great but the lecture material is very brief and short. The references provided throughout the lectures are probably the best source of more information.

Great intro to machine learning. Several algorithms with some ideas on sampling and pre-processing techniques are covered. Adding a textbook as done with some of the other data science classes would help, but other resources are referenced.

Excellent course, very practical. Found the project challenging as preprocessing data required some knowledge of the limitation of the RandomForest method i.e. both train and test needs to have same classes of data with similar levels.

Nice course that tries to fit a lot of material into four weeks. Due to this, the material is not so deep, although pointers are given to where the student can find additional information related to each subject covered by the course.

Pretty good overview of how to build some types of machine learning models through the caret library in R, but not much in terms of the theoretical underpinnings or why one method is better than the other or where it is most suitable.

Some problems with current and old versions of packages and problems with using other packages on different operating systems. Though that did also help foster an independent research style which will help me in the future.

GREAT course! There are all base of machine learning field. The limitation is blur between basic and detail especially maths. This course, sometimes , show the maths that make you confuse if you're not familiar with them.

This course gives an overview of a broad subject. My personal feeling is that there could have been some more indepth examples/case studies to demonstrate how to apply these methods and analyse /interpret the outcomes.

It needs more mathematical detail. Otherwise is a fairly comprehensive class, and a great tutorial on the caret package. I recommend it, if you need to refresh concepts and get some practical exposure to caret.

It is a nice introduction, but the course is not as good as the other ones from the specialization. Nonetheless, it is just right to get into ML, understand key concepts, applications, algorithms and practice.