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

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....


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.

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.

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1551 - 1575 von 2,025 Bewertungen für Maschinelles Lernen mit Python

von Tichaona M

19. Okt. 2020

This course is great but one needs more time to read through other information sources. An in-depth understanding is critical to get the bigger picture of use.


6. März 2019

Pros. Good for running code in Jupiter Notebook environment.

Cons. Language Support only English (others are few), and Screen sub overlays presentation text.


10. März 2019

Excellent course . Concepts of machine learning algorithm were explained clearly and easily.

The interface with IBM notebook could have been much smoother ,


25. Okt. 2019

Would like to say that it would be of great help if we had some more practice on coding. But overall a wonderful course and helped me learn a lot. Thanks!

von Hamsavardhini A

16. Aug. 2021

It was an excellent course. They covered all topics of ML. Practical session was good but need more explanation. That would be very helpful to students.

von Nermin K U

28. Nov. 2019

The final project is ill-structured. It is hard to grade because you need to go back and forth in the codes. It makes both doing and grading harder.

von Aftab R

28. Jan. 2020

The course appears to assume good competency in Python and does not provide much training on Python. This should be highlighted to students upfront.

von Sherbulandkhan B

26. Apr. 2020

Course is very well structured. Some extra guidance and assistance would be nice with the Peer-graded assignment as it gets bit tricky and complex.

von Luis D C

23. Jan. 2020

Learned a lot in this course, I would've liked there were more exercises throught the videos rather than some questions at the end of the section.

von Selmir K

6. Juli 2020

There were a couple of videos where the notes weren't separated with punctuations. But other than that, it was a really good course. Thank you.

von João P d J S d R

29. Okt. 2020

This course is very well, but it doen't have model selection and stratified features selection with sklearn.model_selection.train_test_split.

von Tarit G

29. Nov. 2019

It was an awesome experience to learn machine learning. The instructor has explained every algorithm in a detailed way. It was very helpful.

von Luke P

25. Jan. 2021

Good course if you have some basic knowledge of Python and data analysis. However, much of the course material had typos and small errors.

von Laura S M D

14. Dez. 2019

Un curso muy completo, aunque mejoraría un poco los ejercicios, que al estudiante se le diera más importancia en la resolución del programa

von Jacqueline ( G

4. Aug. 2019

It's so bad when someone reviews your assignment and gives you an unfair score. But this happened a lot because of this peer review system.

von M R F D

4. März 2020

Well Explained. Video lecs are very easy to understand and upto the mark...Assignments little bit need more clarification and explanation.

von Gaurav S

19. Juli 2019

The Course Could have been a little better if there were more theory and more illustrations at time a disconnect was felt in the Course

von Alonso h g

25. Okt. 2021

I think the methodology is outdated. But the bases are the same. It is remarkable that they teach how the algorithm and formulas work.

von Shivam S

7. Nov. 2020

Very fascinating course but exercises like final project will be more for exposure to real coding than it will be really more helpful.

von Roman S

9. Juni 2020

Course content and presentation is really good! The only thing i would add is the tuning of hyperparamaters which makes ML what it is.


3. Mai 2020

Great course but there should be videos where there is need of explanation on code as well, codes given are very good and covers basic

von Mallangi P R

27. Jan. 2020

I really liked the course content, way of teaching and assignments.

This will definitely help a beginner in data analysis to start with

von Beatriz E P

28. Jan. 2021

Very nice course!! You learn a lot more of the theory than the practice part, but the concepts are well explained and I learned a lot

von manasa k

22. Feb. 2021

A good course to quickly learn important aspects of ML with Python. The assignments and final exam is also very useful for learning.

von fang f

11. Juli 2020

quite good at the explanation and un-graded exercises.

But the knowledge could be deeper and more about parameters in Sklearn APIs.