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Bewertung und Feedback des Lernenden für Maschinelles Lernen mit Python von IBM

4.7
Sterne
11,807 Bewertungen
2,036 Bewertungen

Über den Kurs

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....

Top-Bewertungen

RC
6. Feb. 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.

FO
8. Okt. 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.

Filtern nach:

1426 - 1450 von 2,025 Bewertungen für Maschinelles Lernen mit Python

von Marcio F V

15. Nov. 2021

OK!

von Abhijit P

7. Juli 2020

okk

von Muhammad T A

29. Sep. 2019

<3

von Ahmed A M

7. Feb. 2019

ff

von Ali C B

21. Dez. 2020

.

von Carlo E C

8. Okt. 2020

U

von Prathamesh S

5. Jan. 2020

h

von Deepa S

22. Okt. 2019

A

von Uttam K

16. Apr. 2020

Thanking Coursera for providing me the free education and helping for my substantial need

as I was not able to afford the course fee ; literally I can't express the happiness of

mine in words and how much I'm thankful to coursera cannot be described but heartfully am

feeling blessed by the coursera for helping me..Thank You Coursera with Love.

And none the less the instructor was very helpful throughout the course and along with the

discussion forum is also a great way to share and being helped during any problematic

situation but one thing I would like to add the lab tools are not available most of the time

but hopefully got to managed by practicing on my local Jupyter Notebook with the help of

sir's saeed aghabozorgi github repo. As I had some prior knowledge of Machine Learning so

the course was on intermediary level for me on scale of learning and enhancing my

introductory hands-on skills of training .

I have successfully completed the project although it was challenging but enjoyed a lot while

learning and building my final_capstone_project.

I've posted my project notebook very neatly and well maintained and have posted my notebook

with no hidden code cells to help others and inspire with my work.

If anyone wants to visit my github repo to final_capstone_project notebook feel free to commen

t down I'll share it with you happily :)

Thank You !

von Sherry A

2. Juli 2020

The content in this course is presented clearly through the videos provided, and the ungraded labs are quite helpful in learning how to implement the algorithms discussed in the videos. I took this course by itself (not as part of the IBM Data Science Certification), and there was some stuff I had to look up, especially about Pandas data frames and how to work with them. Maybe that content is covered in the courses before this one in the certification sequence. I didn't see a prerequisite knowledge list for this course, but that would be helpful for future learners who are considering taking this course by itself.

The reason I'm giving this course 4 stars instead of 5 is because of the typos that occurred, especially in the directions of the final graded project. I was able to read through the discussion threads about the final project to get a better understanding of what I was expected to do (because part of the directions don't make sense), but those posts are from over a year ago, meaning the typos haven't as of yet been corrected in the course.

Otherwise, I found this course to be enriching and enjoyable! Thank you!

von Sourabh K

10. Juli 2020

The course is not for someone who is new to python. This course requires some prior proficiency and understanding of the language.

There are no professional notes at end of each module or section like some other courses, so you need to take your own notes while going through videos. Having proper summarized notes like the ones in Andrew NG machine learning course would have been great.

There has to be some proper videos / guidance notes or well documented pdfs focusing on the data pre-processing and related components in Python and all other details as well regarding training a model, assignments are directly provided to be completed in Python without any tutorial of the same

Overall a good course but it would be great to have all documentation. Also since the title itself makes it clear that course will be in Python, Kindly add videos to the course which help more understanding of all concepts through Python, currently all videos only have conceptual explanation but no video touches the Python component or how to go about the implementations in real world.

Thanks

von Cameron W

5. Feb. 2021

This course was very informative about the basics of machine learning, the standard ML models and how the underlying algorithms work, and ML process of importing, cleaning, manipulating, and ultimately analyzing data.

The Python aspect of the course is extremely high-level and honestly not that helpful. All the code is pre-written for you and often without full explanations for what its doing. Specifically, all the pre-processing, feature engineering, data visualization, and basic program-building is already done for you, so reproducing it in a real-world setting would be difficult for anyone without a computer science background.

Overall, this is a great course if you have previous programming/data analysis experience and are trying to simply familiarize yourself with the basics of popular machine learning models. If your goal is to learn how to build models from scratch for a practical application, you may want to supplement this course with others.

von Dmitry N

21. Sep. 2019

4 stars only. The course was good. No problem with that. However, IBM keeps to update GUI of their cloud. Instructions provided in this course are obsolete.

Another thing, for someone, who didn’t take machine learning courses somewhere else, the amount of theory presented here is not enough. It is fine that you can put stuff inside your python (yes, I said this), but you have to understand, why are you doing this. You have to be understand how does it work. These libraries is just to try something out fast. The real implementation of the sophisticated algorithms is much more complicated. That doesn’t mean you have to be a PhD to do it, but you have to understand basic math that is going behind the curtain. It’s enough even for one algorithm. How many will know the difference between bias and variance after this course? How many will be able to say, how it can be fixed? Try to answer on this question.

Regards,

von Fausto B D S T

16. Apr. 2021

It seems to me that it covers a lot of ground but lacks in depth. Also, the labs definitely should be harder to guarantee real understanding of the material, in my opinion. After finishing the course, I feel that I have been exposed to good quality material on ML, but I don't feel I have really put it into practice, although I have some code to reference if I need to. The peer review assignment was clear (I had seen a lot of complaints in the reviews, maybe they have fixed it) (this one could also be more challenging). Overall, I think it is still worthwhile as an overview of ML algorithms, applcations and related python libraries.

von Shripad L

27. März 2020

The content of the course is very well designed and it is very easy to follow. The Teachers have done a fantastic job explaining the content.

I would like to make the following suggestions:

There should be more hands on graded exercises. Instead of one exercise at the end, it would have helped if relevant section was graded after it was taught.

There is too much focus on Classification. Machine learning consists of equal parts of value prediction and class prediction. There is nothing on things like Linear regression. It should have at least been included as a ungraded exercise, so that I know what Python functions are used.

von Shashi R

6. Jan. 2020

One of the best course for the beginners who want to learn the machine learning concept from basics along with the theory. The course lecture only contains the theoretical part but the lab part are only being instructed within a notebook link. This course is great but can be improved by adding some lectures of the lab or practical part by specifying how those codes are being implemented. Although the Notebook also explains the best and also helps in learning the practical skill. The assignment given helps a lot in learning the modals easily and visualizing the result.

von Sanchit V P

6. Mai 2020

A very good course to learn the basics of ML. Several in-depth topics are not covered stating that they are out of scope for this course.

The course allows us to use an online tool for lab work and assignments with many relevant libraries, thereby avoiding any software/library installation issues, etc.

There are relatively less number of videos but they are to the point.

Labworks need to be self-learnt(no separate videos for code), although the notebooks that are shared tries explaining the code a bit.

Overall for a new learner in this field, it's a good start.

von Julio E F V

9. Sep. 2020

Marking my score as a 3.5 as I cannot choose fractions:

I think the course is fantastic from the academic point of view, I had taken courses from other sites and this one clarified all doubts I had in regard to the mathematical nature of each of the studied methods.

The missing star (and a half): little to zero explanation on the algorithms. Yes, it poses the challenge of self studying but at the same time I believe some codes might be to advance for a person with average exposure to the language to figure them out by themselves at a reasonable pace.

von Shernice J

30. März 2019

The elbow method for evaluating the best K in KMeans was mentioned in a video but wasn't demonstrated in the lab. You can find information on it online so its not a big issue but it would have been nice if it were included. Another method, the silhouette score, could have also been mentioned. Overall the course was very comprehensive but if you want to get the most out of it you need to make sure you understand all of the code in the labs which can take some time and research. Some more documentation of the code can really go a long way.

von Dr_G@ur

28. Juli 2020

The course nicely introduces the learners to Machine Learning, it's commonly used algorithms and it's applications in various fields (which is the best part!). It will surely help budding Data Scientists in getting insights about Machine Learning and it's working principles. Instructors are awesome and so are the videos.

Though labs can be made better for people with no/little programming background, I would still suggest this course to learners interested in the field of Data Science. A good one for sure. And definitely interesting!

von Rami L

27. Mai 2020

Mostly a very nice course introducing the basic ideas behind many standard techniques together with the basics on how to implement them. Gives a good start to learn ML further. One star lost from the fact that some of the quizzes are badly designed -- multiple choice questions with slightly ambiguous answer possibilities where you get no partial credit nor any feedback on what went wrong. I still have no idea why some answers were right or wrong as I just had to try too many different quesses to get a passing grade.

von Adrian I

11. Sep. 2020

Great video material and clear structure. I also like the JupyterLab integration. The exercise notebooks need some cleaning up though: Lot's of grammatical errors, inconsistent coding conventions (snake_case vs camelCase), poor variable naming, programming mistakes resulting in incorrect accuracy scores, outdated libraries (there are provided functions for rendering confusion matrix and plotting decision trees in sklearn, which could be used). It shows that the notebooks have not been created by Python experts.

von Jie-Yu L

11. Aug. 2019

I really enjoy this course. It teaches me a lot of basic machine learning model, method and data analyzing technique. However, I still recommend that it should have coding assignment for every week exercise. It is because learning from video is simple but hard to do implementation. The best way to learn data analysis is to implement or do the real stuff by ourselves. It is necessary to put an assignment to force every learner try and error. This is my opinion for this course.

von Andrew B

1. Juli 2019

The rubric for the last assignment was too arbitrary. People with little to no machine learning experience will assume that submissions have to be cookie-cutter copies of previous labs in order to achieve 100%. I would put force students to put random seed on models in order to achieve similar results to achieve more homogeneity and therefore an easier way to grade. Perhaps you could put a section at the end that allows for further parameter tuning if the student so desires.

von Martha C

16. Apr. 2021

The course is well done and covers many of the basic ML concepts. The reason I gave it 4 instead of 5 stars is because the final assignment asks you to do something that wasn't covered in the course, and it's not very clear either what they're asking you to do. I was able to figure it out, but it was a bit frustrating at the time (especially since I got all the way to the end and realized I had to do something different).