学生对 约翰霍普金斯大学 提供的 实用机器学习 的评价和反馈

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.

It's a great course but I hope you add a few things. The course about the machine learning algorithm is so basic. Please get deep into the machine learning algorithm. Then it would become the perfect course.

In my opinion this course is highly technical and demanding in nature compared with the others. The learning experience is good and coursera.org has given a opportunity for customization ! thank you Coursera

Very good summary of strengths/weaknesses of various machine learning algorithms. This lecturer's style and production quality is much higher than in the previous two courses in the specialization series.

I learned a lot in this class. There are slight gaps from the depth of material covered in the lectures to the quizzes and assignment. If you're good at researching online, you'll be fine.

It is still kind of hard for a learner to understand the methods. But it gives me a overall introduction of machine learning and I will have further learning in the future.

Lots of good material, but some things (like PCA) didn't receive enough coverage in the lectures. The quizzes also weren't great at testing the material in the lectures.

Great course. Only missing piece is the working information / maths behind the models. But as the name suggests it teaches practical approach towards machine learning.

Not as detailed as some others in the specialization which is a shame but good none the less. The videos go through the info quickly so be prepared to go back over.

Great as an introduction for someone with no practical experience. Lectures are too theoretical and lack some examples to translates the theory into practice

Very good course. Fast paced and a lot of self study required to fully understand some of the nuances of the R (if you're not familiar with the language).

Great course, very high paced with a lot of information. would have been great to add two more weeks and another project to use more machine learning

Rather basic, nevertheless a good introduction to the topic of machine learning with R. Mostly concentrated on applications of the R caret package.

A very good starter course on Machine Learning in R with great links to various resources that students and delve deeper into the various topics.

Good Course the covers a lot of practical aspects and relevant to the real world solution.

Good References and Learning Materails are available

Good class to get the basics of Practical Machine Learning. This course is best taken as a part of the data science series from John Hopkins.

It's harder than the previous one. it would be nice to update some the quizzes as they are based on older versions of R Studio libraies.

The quiz should be constructed in a way that depends less on the version of the libraries used. The rest of course was excellent.

Vary practical approach, almost no theory or in-depth explanation of the subject, but a lot of focus on applying ML in practice

To me with this course, the best learning aspect is the final project; how to use Machine Learning Algorithms on data analysis.