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Отзывы учащихся о курсе Практическое компьютерное обучение от партнера Университет Джонса Хопкинса

4.5
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Оценки: 3,010
Рецензии: 572

О курсе

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation....

Лучшие рецензии

JC

Jan 17, 2017

excellent course. Be prepared to learn a lot if you work hard and don't give up if you think it is hard, just continue thinking, and interact with other students and tutors + Google and Stackoverflow!

MR

Aug 14, 2020

recommended for all the 21st centuary students who might be intrested to play with data in future or some kind of work related to make predictions systemically must have good knowledge of this course

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326–350 из 563 отзывов о курсе Практическое компьютерное обучение

автор: Rudolf N

Dec 19, 2015

Thank you for inviting me to be a beta tester for Practical Machine Learning. I completed this course at the beginning of October of this year. When I was asked to be a "beta tester" I thought that I would be presented with new materials. However, the only thing that has changed is the look and layout of the Coursera web pages. The video lectures, quizzes, and assignment are the same as they have been for quite some time. Here are some specific comments:

1. The video lectures: To me, these are clear and easy to follow. However, like those in the other courses in the Data Science Specialization, this course covers a wide range of subjects but tends not to have much depth. When I compare this and other courses in the specialization to other moocs that I have taken including Machine Learning with Andrew Ng and the Stanford Online EdX Course Statistical Learning with Trevor Hastie and Rob Tibshirani, the somewhat cursory treatment of the topics in the Data Science Specialization becomes more noticeable. Perhaps in the interest of "truth in advertising" this course should be called "A Brief Introduction to Practical Machine Learning." In the interest of full disclosure, I should note that I have an undergraduate degree in economics and an MS and PhD in psychology with a quantitative bent. I have had lots of statistics courses, especially those related to ANOVA, MANOVA, nonparametric statistics, correlation and regression methods, and structural equation modeling. The latter is important in psychology because researchers in this field like to measure latent variables. I had been an analyst using SPSS for several decades and the courses in this specialization helped me to migrate to R. Also, there have been may new developments that have become more accessible through R packages (like the fancier tree methods) that were not available when I completed my PhD. Thus these courses (and others such as the ones by Ng and Hastie and Tibshirani) have helped me to keep abreast of these developments. So they are good for me, but I wonder to what degree do the courses in the Data Science Specialization actually make a person a "data scientist?"

2. The quizzes: I think these items are good practice and are at a reasonable level of difficulty. However, these items are the same ones that you have been giving for quite some time, with perhaps a few new ones added. A little googling will lead you to the answers to these quizzes posted online. I recommend that you put a little time and effort into writing all new items.

3. The final project: Again, this project is good practice and seems to be at a reasonable level of difficulty. And again, this is the same project that appears to have been given at the end of numerous iterations of this course. And again, numerous write-ups for this project can be found online. And again, I would recommend that you put a little time and effort into finding a new data set for people to analyze. This would help minimize some of the rampant cheating that I found in this and in other classes in the specialization.

On the subject of cheating, when I was doing the peer grading for the courses in the Specialization, I would enter the code of the students that I was grading into the Google search box and all too often I found links to submissions for the project by students who had taken earlier sessions of the class. That is, students were copying these earlier submissions by other students and submitting them as their own. And I don't mean that they were similar: students were copying other people's work line by line, character by character. I found that to be quite irritating and I always reported it to Coursera. Of course, if the instructors would change their assignments once I a while, then this sort of copying would be impossible. As it is, it appears that the good professors put a lot of time and effort into creating what are indeed a worthwhile set of classes. However, after they created the classes, they seem to have pushed the "autopilot" button and gone off to do their day jobs. I would suggest that re-engaging with these courses and reading some of the comments that other students have made would be helpful.

Overall, I appreciate the courses in the Data Science Specialization and specifically this course. I know that these class materials took considerable time and efforts to create. I wish the instructors continued success with these classes.

автор: Robert O

Jul 27, 2017

The course subject matter was great but like the course 6 & 7 scenarios i found the lectures didn't reiterate or reinforce key takeaways that are easily confused. For example is cross validation when you split the data into a training and testing, when you have a separate unknown results set to test final training model on. Or does it require doing folds and then breaking each of those up into training and testing chunks. Or why is it not okay to use a model training function that internally does cross validation similar like randomForest documentation suggests. Also things like what the prediction accuracy implies in contrast to the model oob [ in ] sample error estimate and if that estimate is akin to the 1 - prediction accuracy on test data set, i.e. out of sample error estimate. Seems like liitle coverage was given to whether or not there are well known training models to use or if you literally need to try and compare the 1/2 dozen or so common ones out there every time to find out which one to use for a given dataset. Also left confused about overlapping use of words classification model training, i.e. are they synonyms for the machine learning based functions we use to try and fit models to data.

автор: Jason M C

Mar 29, 2016

Of all the JHU Data Science specialization courses I've had, this was by far the most enjoyable. I really liked how the class was more in the style of 'here's some techniques, now do whatever you want on the project.' Prior courses are, and understandably so, more constrained in the assignments. It's not until here that the student really has the tools to be able to flex their analytical muscles, and it pays off.

Also, of the three instructors, I am most favorable to Jeff Leek, who teaches this class. He communicates much clearer than Roger Peng or Brian Caffo. I find I learn more from his content than the others.

Lastly, I will say that this class doesn't hold a torch to University of Washington's Machine Learning specialization. That's expected since this is one class and that's a whole series of classes. If you're hungry for more after this one, I highly recommend UWash's Machine Learning specialization.

автор: Sheila B

Aug 09, 2018

I've been working my way through the whole track, and this was by far the most complex material--but it was easy to understand because the videos were so clear.

I do have one bone to pick, though: the quiz material relies on very old packages. Again and again I had to finegle something so I could answer a quiz question. That makes you guys look like you are lazily sitting back collecting money but not really doing your job as far as teaching goes. It's time for an update. How hard is it to run your quizzes on updated packages and offer answers that are current?

Aside from that, I find that you explain material very clearly and you are my first choice for picking up a new data science skill.

автор: Andrew K

Mar 13, 2017

So why four stars vs five stars, of all the Data Science Certification courses that I have taken: i) some of the examples and quiz challenges don't work as they should, ii) Machine Learning is rapidly changing area - should be updated to reflect this and perhaps a high level taste of Deep Learning, iii) posting the Final Project is overly complicated relative to methods of the other courses - this should be cleaned up - still not clear how point to a github repo link and also have a rendered html page working from that same link - requires two links to present materials and must use default names like index vs. a project name.

автор: Terry L J

Nov 09, 2018

Lot of good material, however, on all of these courses, it would be very helpful if they were better organized for learning.

Overview of learning objectives in a step sequence for a more organized approach for learning (maybe even a process roadmap map sequencing activity to follow that you can reference back to.

Detailed information for each step in the learning process that can be followed that maps back to the roadmap.

A summary of the learning objective in the roadmap sequence.

Basically, just like writing a paper, > overview/objectives > Main topics >subtopics, etc. > summary

автор: Chris M

Aug 14, 2016

Unlike the rest of the modules in this specialisation, this one was well taught, a good blend of theory and practice and well paced.

There were still a few issues with wording in quizzes (and some where there seemed to be two identical answers to one question, where one would be considered right and the other wrong - purely chance). In addition, the lack of consistency in how to submit assignments across the specialisation is frustrating, I'm not sure if it's supposed to be a way to show how to use github or something like that, but it shouldn't be the case.

автор: Carlos M

Jul 12, 2017

A good course, but the field is so large and so important. You'll really need the "hacker" mentality to get through this course. They DO NOT teach you even close to everything you'll need to complete the course. It's also very stats/math heavy which will make the theory difficult. This isn't why I only rated 4 stars. I did so because of the lack of Swirl and the feeling that I still don't feel like I understand the topic well enough to do anything in a business setting yet. I was hoping for more from the class.

автор: Moiz

Feb 03, 2017

By using the caret package, this course took a very pragmatic approach towards machine learning. It demonstrated how to perform all the essential tasks in making the machine (algorithm) learn from data.

In my case, this course required a dedicated time commitment for successful completion. In addition to course lectures, i used the 'Machine Learning with R' book to fill my knowledge gaps. Overall i feels that this course helped me in my journey of gaining a better understanding of this subject.

автор: Yukai Z

Dec 09, 2015

A good introductory course for people who has an interest in knowing the principles of machine learning and want to make a step forward. Sufficient details covered throughout the course and additional resources were provided which are very useful. Quizzes were well designed with minor improvements in the accidental mismatch of the answers due to package version issues. Overall the study experience was enjoyable and would definitely recommend to someone who wants to start knowing data science.

автор: Romain F

Mar 22, 2017

Good course on the whole, learned a lot and enjoyed it, but it would need to be updated and corrected (certain bits of code don't work as they did when the course was produced, which can be pretty confusing). Would be nice also to add some more content at the end of the course : the lecture about unsupervised prediction felt rushed, and a proper conclusion opening up to the rest of the field would be useful. Anyway thanks again for this wonderful learning opportunity, keep it up ! Cheers

автор: Carlos S

Jan 31, 2016

First and foremost I'm so thankful for the exposure to so much material in such a condensed schedule. Very good class. Even though I had to muscle my way through it.

I think the class could be improved with one additional discussion thread for the project.

A guide similar to the ones created for Inferential Statistics and Regression would also have been very helpful.

I benefited immensely from reading parts of the book "An Introduction to Statistical Learning" while taking this course.

автор: Robert K

Nov 14, 2017

I realise that the course is practical machine learning, however I find myself wondering more about the 'whys' than the 'hows' after the course! Still, much benefit and many useful concepts covered which can be revisited in greater detail down the track.

I would also like to see the final assignment change subtly every so often as there are existing completions on the web and it's too easy/tempting for some to simply copy and paste.

автор: Vathy M K

Aug 13, 2016

It's very cookbook driven - it's not a deep dive into the topics. This can be dangerous: a little knowledge and all that. However references for more are provided. If you can imitate the coding examples, you should be OK for the assignments. Fair warning: the quizzes are hard to replicate unless you set up your environment to mirror exactly the version of the packages used in the course.

автор: Siying R

Nov 27, 2019

This instruction is better than the last one because he can use examples that people from outside the medical world can understand. The quiz is harder than the final project. It requires students to do extra work to figure things out. I see the pattern where the instruction really is the door holder to you and you need to walk in the room and find what you need.

автор: Jikke R

Aug 11, 2016

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.

автор: Sean Q Z

Dec 11, 2016

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.

автор: Charles W

Dec 08, 2019

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

автор: Jorge E M O

Sep 07, 2018

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.

автор: Brandon K

Mar 30, 2016

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.

автор: Oliver S

Jul 26, 2019

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.

автор: Lukas M

Oct 06, 2017

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.

автор: Robert S

Sep 16, 2019

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.

автор: Lucas

Jun 03, 2016

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.

автор: Swapnil A

Jun 09, 2017

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!