Chevron Left
Вернуться к Машинное обучение с использованием Python

Отзывы учащихся о курсе Машинное обучение с использованием Python от партнера IBM

4.7
звезд
Оценки: 9,415
Рецензии: 1,518

О курсе

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed! By just putting in a few hours a week for the next few weeks, this is what you’ll get. 1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy 2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more. 3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course....

Лучшие рецензии

RC

Feb 07, 2019

The course was highly informative and very well presented. It was very easier to follow. Many complicated concepts were clearly explained. It improved my confidence with respect to programming skills.

RN

May 26, 2020

Labs were incredibly useful as a practical learning tool which therefore helped in the final assignment! I wouldn't have done well in the final assignment without it together with the lecture videos!

Фильтр по:

1426–1450 из 1,505 отзывов о курсе Машинное обучение с использованием Python

автор: abd-elrhman m

Jul 05, 2020

everything was out of scope it was just a brief of every thing

автор: Aravind P

Jun 12, 2020

Theory part was awesome. But not much of practical knowledge

автор: Stefan A

Jun 06, 2020

Lab is working bad so a lot of time is waisted with waiting.

автор: Ramsrinivas A

Jan 17, 2020

Theoretical portion was shallow compared to Lab portion

автор: Hunter W I

Apr 21, 2020

Learned a little bit, want more real world application

автор: SHAONI C

Dec 02, 2019

needs more clarification on classification algorithm

автор: lorenzo a

Feb 12, 2020

soooo many typoes and small mistakes in this course

автор: H A H

May 19, 2020

this is good course about basic machine learning..

автор: Haykel S

Dec 26, 2019

The Correction of assignment is not very correct.

автор: Long N

Aug 15, 2020

The course should have been designed better

автор: Abdul R

Mar 28, 2020

Labs Are not responding it sucks a lot

автор: dk

Nov 23, 2019

不建议新人学,这个是系列课程的一部分,内容不多 只讲个大概给你听

автор: Omkar A

Jun 17, 2019

Practical Classes were Missing.

автор: Manoj P

Oct 30, 2018

can be done much better

автор: Rao M H

Apr 01, 2020

Lab are working worst

автор: Rajesh K R

Dec 12, 2019

Good for beginners

автор: Сокол С А

Dec 02, 2019

Too superficial

автор: Farrukh N A

Jul 15, 2020

I have just completed the course and mentioned below are my key pros and cons for this course:

Pros:

1) I loved the theory and different techniques explained in the course.

2) The presentations were very well made and it helped me to gain knowledge as far as ML is concerned.

Cons:

1) This is a pretty outdated course, where there are ALOT of typos and coding errors throughout the labs as the coder has left IBM and is working in some other company for more than a year now. Thats is why no one is there to update the course.

2) The title of the course should be "Machine Learning with Mathematics" rather than "MAchine Learning with Python" because the emphasis of this course is on using mathematics to solve ML related problems and that is why most of the libraries and techniques used in the python files were not defined.

3) This IBM's specialization is of BEGINNER level and the inclusion of an INTERMEDIATE level course which requires you have to have some experience in Data Science and advanced level knowledge of Python is just mind boggling to me. It would have been great if a basic level course of ML would have been developed which emphasized on explaining while using Python libraries would have been much more appropriate for us.

4) Lastly, it has confused me while going through this course that numerous times the lecturer spent major time of the lecture in explaining the advanced mathematics which Pythons libraries can easily do for you, even if he told us that remembering of the mathematics is not need. STILL he explained it. I don't know why he did it again and again.

автор: Lyn S

Aug 23, 2019

It's too bad some people with phds and very poor teaching skills think they can write up some code and feel they are teaching these classes. That being said, it's super cheap and it's very easy to find information online to supplement the lack of adequate descriptions of the topics. Changes that would make me more likely to take another coursera class :

Don't have a bunch of really short videos, combine them into one longer one.

If there is text or code on a slide, make sure that is in the transcription.

Don't have the dumb popup questions that stop the video and make you find the mouse and click to restart the video. Many of us are listening to the video doing something else, I listen over and over. Sometimes, I have to read the transcription to understand what is being said, so I have to stop, get the mouse, click back up to the slides, press SKIP, etc...

If you have an exam, make sure to later send us the answers - e.g. the code that we were expected to write. This is the weakest and most frustrating part of this class. I was not sure how to some things, in part because I wasn't sure what was being asked, to what detail. Even the class discussions showed we weren't sure what data set to use for what. It seems to rely on peer grading, but most of the responses I got from peers was either completely absent or not useful. But thanks for keeping this relatively cheap.

автор: Fangfang K

May 17, 2020

I learned a lot from this course. However, had I known what I had to go through to learn the knowledge, I would not have taken the course; the process is too painful. Therefore I would not recommend the course to future learners. Read my review and save yourself $39.

1) Too many typos, bugs, inconsistencies throughout the videos and labs. The same mistakes have been brought up by students over and over again on the discussion forum, but have never been fixed.

2) Teaching staff do not pay attention to students asking for help. Sometimes when they do answer the question, they give a very vague or irrelevant answer; and when being pointed out by students that their answer is not helpful, the teaching staff do not bother to reply and address the issue. I feel like the teaching staff never went through the entire course themselves so they do not understand our students' concern and frustration.

3) A lot of Python codes are never explained or commented. This is a beginner level class but they expect you to be able to code proficiently; otherwise you are going to be stuck with one line of unexplained code for a long time...

4) The whole course is like a giant advertisement for IBM Cloud, which is not user-friendly at all.

автор: Tom S

May 13, 2020

Like many of the courses, the instructions are not in a format that supports incremental learning and focuses on the mechanics for performing an activity rather than an explanation for why and the reason we are doing these things.

The objectives and measures of success for the final exercise is not clearly articulated, causing me to guess as to what the evaluator had wanted us to do. The instructions said to solve for the four types of methods, but left it to the student as to if they wished to generate graphics, etc. If the only objective was to generate the Jaccard score, F1 score, and LogLoss (as appropriate) to complete the activities, then it should have been stated. In addition, the examples presented in the course labs did not have us generating the F1 and Jaccard scores for many of the models.

автор: Alexander W

May 07, 2020

Even for an introductory course most lessons lacked depth. Usually the broad idea of an algorithm is introduced and then an exercise shows a python call to which applies it. However neither are there any theoretical/mathematical insights why the algorithm works, nor does one obtain relevant practical knowledge. E.g. the course fails to even superficially explain the many options and parameters each algorithm has and which are necessary to actually apply it in practice.

What makes it worse is that there is apparently no support and maintenance for this course: There are tons of smaller and some larger mistakes in the lectures as well as the exercises, however reports of those as well as most other questions in the discussion forums remain unanswered.

автор: Reha P

Jul 19, 2020

This course was definitely informative, but the final assignment grading process was ridiculous. There was way too much ambiguity with the grading criteria. I submitted the same exact assignment twice, the first time I got a 13.5 and the second time I got a 25. This should not be possible. Much like some of the other courses in the IBM Data Science certificate program, I HIGHLY suggest adding an image of what the solution should be instead of leaving it up to people to determine what they think is right or wrong. This turned into an all day process for me and I'm beyond frustrated with the course and relieved I'm done with it.

автор: Ankit K

Jul 27, 2020

Very deep with less, almost zero explanations. Not at all for beginners. Either, it has been given as an overview or should completely moved to Professional Segment.

As I remember, at the very first starting of this IBM course series, it was quoted that you need not to know much coding, but what I am observing by end of the modules, it requires lots of coding.

There must be specific guidelines what to learn, what to master before attempting, otherwise it just becomes a mere certificate.