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

12,644 Bewertungen
2,196 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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1601 - 1625 von 2,195 Bewertungen für Maschinelles Lernen mit Python


8. Apr. 2021

The course is very useful for the beginner as me. It contains a good math explanation of ML methods. The technical Python part in labs is good enough too for the beginner in Python/ Sometimes it don't correspond fully to the math part but it isn't critical. Thanks for the course!

von Edwin S

25. Mai 2020

Good course syllabus. Some improvements needed: Jupyter notebooks contain many English typo errors. The final assignment rubric uses the wrong normalization technique for the test data where test data were normalized by itself instead of training a scaler with the training data.

von Fabrizio D

20. Juni 2020

I noticed some (not critical) mistakes here and there during the video lectures and the quizzes.

Over all, a good course, but I think in order to gain a full understanding of the material one needs to look deeper into the literature. The course provides a good starting point.

von joe b

13. Okt. 2020

Working with the IBM notebook for the final assessment was a bit of a pain

And the peer assessment has no recourse, someone marked me down for something i had, i guess they didnt notice? but i dont have an easy way of getting that changed

Content is good stuff though

von Pratyush R

28. Feb. 2022

Very Good Crash Course. Concepts are explained efficiently without wasting much time on the Mathematical/Statistical stuff. Awesome Notebooks are given to code along with the concepts which is really helpful. Must learn deeper and further to become a Professional.

von Martin E

2. Apr. 2020

Content was good, but mainly classification. I was missing other aspects (like regression, deep-learning, ...). What really annoyed me a lot was the constant advertisement of the IBM infrastructure; for this course the IBM Watson thingy is largely overblown imho.

von andrew r

16. Nov. 2020

Covers the basics and explains clearly the difference between regression and categorization. Lab work was instructive. Some of the material is now out of date, contains grammatical and spelling errors, and has inconsistencies with instructions and testing.

von Tauseef A

2. Juni 2021

The hands on lab are not worth it, there is very less explanation about why we are doing particular step, take for eg final peer graded we just put Boolean value based on just the attribute value of master or above. Very poor last project to be honest.

von John V H

14. Nov. 2019

I liked the course overall. Some of the lectures did not break down real world data sets or examples as much as I would have liked. Additionally, it would be nice to have more real world data set examples or tutorials to study or analyze with Python.

von Michael L

23. Okt. 2019

The labs are great and the videos are spot on. However, there are numerous typos here and there and also the final project grading rubric had some issues and did not provide some people with guidance and submissions that were correct were marked wrong,

von Rick K

22. Feb. 2022

The course information was good. I wish the videos would have touched on the pythonic code for these examples, even just a little bit. The bad was that the labs were down through the majority of my class which made it difficult to see the python part.


7. Aug. 2020

All classes was very well designed and structured. In my opinion was the best course I done by coursera. The inconvenience was due to IBM Watson. The Lite service plan offers 50 free hours of free use and I received 10 as the time limit.

Thank you.

von Miele W

21. Dez. 2019

A very good course to grasp the foundamentals of Machine Learning using python. Besides the math explanations, i reccomend to have at least a basic knowledge of python, in order to explore the jupyter labs which, in my opinion, are solid examples.

von Sean L

31. Aug. 2020

Really interesting course. I would have enjoyed if it went into more depth in some of the topics, for example being more specific with certain algorithms. Would also have liked a peer graded assessment on multiple topics (not just classifiers).

von Tom C

12. Aug. 2021

Excellent overview of machine learning in Python. Only reason I didn't give it a 5 is because I would have liked a little more content introducing more of the sklearn library in order to be better prepared for working on the project correctly.

von Karthik C

4. Aug. 2019

Hi all.I am so glad to participate this course this course provide me the practical exposure of the machine learning.And Add a credit to my resume and increase the ability to build a "ML model". Great and earn a certificate.from IBM is worthy

von Cynthia P

17. Sep. 2020

This was the meat of the IBM data science course set for me, and was really very informative. Extremely well presented and clear. I would have liked a bit more depth in this material, with a bit less emphasis on python/sql/tools issues.

von Ramakrishnaprasad

17. Dez. 2019

The Course is very valuable content for beginners and easy to understand, the explanation is very good with simple words and live examples. i had refereed this course to my friends to improve their technology stack, their feeds is also good.

von Olivia D

16. Juli 2020

More interactive questions for the programming exercises. Also, the peer marking has room for error since we can't always identify mistakes in others code easily. A code that checks answers for each point and gives feedback would be better.

von S C

22. Feb. 2019

Good introductory course for people to start off with Python. This course touches upon various aspect of the coding language and the lab environment made it easy to practice things. Looking forward to such informative courses going forwards

von stephane d

1. Feb. 2020

4 stars = Great Course!

missing start = Cloud Management / course interrupted because of month credit expired, expected promo code never available...

Suggestion : Cloud "Assets" available for the entire course without a stupid limitation


7. Apr. 2020

All over about the course is good but little bit math behind the algorithm was not explained (iterative) and implementation with python also not discussed. Over all you will get to learn many things from this course.


All d best

von Binod M

29. Juni 2020

I was not satisfied with the way my final assignment was graded wrongly without any feedback. But overall the course is definitely helpful in introducing machine learning concepts with implementation using popular python libraries

von Phalin D

2. Juni 2020

The content in the course is very detail and clear. They illustrate each time difference technique where we can use in machine learning. Though, I found the exercise in are a little bit easy, but it's help a lot with learning.

von Venkatesh K

3. Juni 2020

Best course to understand basic working of all algorithms. Assesments are goof for fresher and looks easy if one knows already. Ensemble techniques should also be included in this such as RandomForest and Boosting Algorithms.