Chevron Left
返回到 使用 Python 进行机器学习

學生對 IBM 提供的 使用 Python 进行机器学习 的評價和反饋

9,949 個評分
1,637 條評論


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....



Oct 09, 2020

I'm extremely excited with what I have learnt so far. As a newbie in Machine Learning, the exposure gained will serve as the much needed foundation to delve into its application to real life problems.


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.


1451 - 使用 Python 进行机器学习 的 1475 個評論(共 1,621 個)

創建者 Cristian C P Á

Nov 15, 2019


創建者 Shalini S

Sep 06, 2020


創建者 Zakir H S

Jul 19, 2020


創建者 Sudhanshu R

Jun 12, 2020


創建者 Tejas S

Apr 28, 2020



Dec 26, 2019


創建者 Lakshmi N

Dec 11, 2019


創建者 Yangala R G L S S K R

Jul 17, 2019


創建者 piyush s

May 19, 2020


創建者 Pagadala G s

May 18, 2020


創建者 Norma L

Oct 26, 2020

There are some labs that are amazing (towards the end) with all the steps explanations and all, but there are others full of errors, without answers, without explanations.

Even the sample notebook for grading your peers is wrong when it uses the split X_train, y_train for training the set after having found the best K, but then as well for all the other algorithms, and in a 1 year old post even a teaching staff agrees with this.

Also final lab is not properly explained leading to people not understanding what they need to do and resulting in very poor final projects

I´ve enjoyed the course anyway, because I´m more than capable of see what´s an error and what´s not and to find my way through all the flaws by digging in the internet and all, and because I love the subject

But given that we pay for the training, and many of this errors have been highlighted for months and even more than 1 year, I dont get this not being sorted.

Also the lack of support of the teaching staff has been amazing...

創建者 piyush g

Feb 08, 2019

Though this course is a good introduction to machine learning concepts, but i believe it was a little superficial about the inner working of the core concepts( evades the relevant mathematics on many occasions).

What you will learn: An overview of the working of various elementary ML algorithms from data wrangling to implementation.

What you won't learn: The maths behind various learning techniques.

Suggestions to improve: Implementation of the Algorithms from scratch, emphasizing the mathematical background of each technique would help a lot to the first time learner, though it might narrow down the target audience a bit, but would be much beneficial to those who are willing to put some extra hours to brush up those requirements at their own end.

創建者 Areeb A

Aug 06, 2020

This course excellently explained the mathematical and theoretical foundations behind some of the machine learning algorithms, but how to program these algorithms in Python was not explained in the videos and it was left to the viewers to learn themselves in coding assignments, which is the disadvantage of this course. I was just able to do it because I previously had learnt upto some extent from some other websites.

So my advice is that if you still want to take this course, then after learning python, learn python libraries of Pandas, Numpy, Scipy and Matplotlib, and after that learn the sklearn libraries along with some theoretical background, and after that enroll in this course.

創建者 Muhammad A S

May 27, 2020

The difference between teaching and taking quizzes and final coding assignment is too big because you make it optional to see the coding in the lectures and in final assignment you give a huge assignment which is technically not equivalent to the teaching process. So, my advice is that please make the lectures more attentive or make the programming exercises more compulsory and more suggestion and hints to understand it better, so that we can actually do the final assignment on our own. I have completed 8 courses of IBM Data Science specialization, believe me I have faced this issue in almost all of them.

創建者 Niko J

May 18, 2020

Great course for learning ML with Python BUT includes surprisingly many mistakes and typos. Even in the final test there are very misleading copy/paste type of error in the description of the assignment. And many students in the forum have point out those mistakes already two years ago. Not fixing those clear and well reported errors is weird move from the creators and stops me giving more than 3/5 for otherwise superb course.

創建者 Eric G

Dec 04, 2019

The parts on regression are previously covered in other courses that are part of the IBM Data Science professional certificate. Overall, there is a lot of information covered in this course but it feels rushed and done in not enough depth. It is an ok course for an overview of machine learning methods, but sits in a weird spot of trying to be too broad while being detailed, but too shallow for a rigorous study of each method.

創建者 Alex M

Jul 21, 2020

I understand that this is a higher level course, so it may be designed in such a way to require learners to take bigger leaps, but I did not feel the explanations of what was required on the final were very clear, and once I graded other people's finals, it was clear that it was not clear for almost anyone.

Not a terrible course, the material and the topics were good, but better explanations are needed, I think.

創建者 Advaith G

Sep 21, 2020

While the course does give a pretty good introduction to the concepts behind most machine learning algorithms and enables us to realize how ML works, the problem lies in the code. None of the code is explained in detail, so the course is extremely theoretical. It basically tells you to copy the code for your own use with small edits but does not explain how to write the code in the first place.

創建者 Ankur G

May 18, 2020

A good course to learn know-how of Machine Learning using Python language so as to facilitate analysis and visualization of data to make effective decisions. I thank the professors to make this course interesting and worth it. Only thing is, videos can be made in a better way so as to facilitate people with non programming background. Maybe some basics of programming would help.

創建者 Harry T

Jul 14, 2020

Good introduction, but not complete.

The course does well in introducing Machine Learning, and covers a good range of classification algorithms. However I feel doesn't go the full length. The labs very briefly cover implementation but I find that it falls short. There's a lack of polish in the material, while typos are minor, the labs are can be jarring and hard to follow.

創建者 Sergio T

Jul 15, 2020

The course presents a useful overview of basic machine learning techniques without going into mathematical detail. The weekly test questions can be improved to assess the non-qualitative aspects of the topics covered. Using scikit-learn is well illustrated by labs using Jupyter Notebooks. There is plenty of room to update and improve the contents.

創建者 Sean D

Feb 10, 2020

Very much enjoyed the course and am thankful for the great content, however the peer-grading process created some unnecessary headaches. On how to improve this I posted in the forum here:

Thank you nonetheless for a great course!

創建者 Syed F A

Apr 18, 2020

This course provides a great introduction to machine learning. The first 3 weeks are in detail and well explained. The 4th and 5th weeks are not explained as expected. The Labs helped a lot in understanding the practical implementations of the algorithms. However, there should be a little explanation of what is going on in the code.

創建者 Marcel V

Jul 18, 2019

Material covered is substantial.

You get a good overview of machine learning and some algorithms that are used. (Not always in depth.)

My biggest problem with the module is with the end assigment which is not clear in my opinion (and of some fellow students in the forums who also passed this module) This unclarity is not addressed.

創建者 Juan D M G

Jul 21, 2020

Me gustaron mucho los temas del curso! Los videos son buenísimos para entender la teoría; sin embargo, en los laboratorios no está documentado el código y hay muchísimas funciones nuevas que son usadas y no hay ninguna aclaración de cómo se usan o para qué se usan. Sólo en un laboratorio encontré todo documentado y explicado.