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

12,843 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....



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.


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.

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1651 - 1675 von 2,234 Bewertungen für Maschinelles Lernen mit Python

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 Sankha 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.

von Federico P

7. Dez. 2021

Good content, but a few technical issues with Lab.

Possible improvement can be having lectures also about coding ML models in Jupyter, rather than just having theory lessons.

The provided notebooks are well written and clear

von Sai S

13. Aug. 2020

Great Course to get started with the practical Machine Learning, This course is for beginners who wants to get to know the Machine Learning Concepts and its implementation.

Great Step for the next courses like deep learning

von Shubham S

22. Juli 2021

This is a good course for catching up with fundamentals. Although most of the techniques and algorithms discussed here are not widely used nowadays, they are still good to know and useful for simple and small datasets.

von Sen Y

23. Juli 2020

Very informative, I learnt a lot about model training and machine learning techniques. However I found some parts of the materials were jumping too fast to result i.e. not enough step to step explanation for the codes.

von Dominic M L C L

14. Mai 2020

One of the better courses in the series. Lab sections can be better with more practice questions. Final project could have been more comprehensive as well instead of focusing on just one section in the entire course.

von Rahul C

18. Juni 2020

A good course to start your AI journey with python and scikit learn. Four stars because code should be explained in a video, but it has an advantage that when you search something you always discover something new.

von Rohan B

25. Juli 2019

This course provides excellent practical implemented datasets which gets you started but a person willing to do this course must have to learn various things on his own as well to completely understand this course.

von Tim d Z

12. März 2020

Videos contain great content, are very clear and to the point. However, the malfunctioning Lab environments really took the speed (and fun) out of the course. Overall it was an interesting and valuable course.

von Ameer M S

24. März 2019

if only financial aid was available for this course it would have been awesome, the content is pretty good, but the labs are pretty confusing as I haven't been able to figure how to register them as completed.

von Diego I

18. März 2020

Es un curso muy completo que cubre muy bien los fundamentos básicos sobre machine Learning. Al final de este curso tendrás una noción de que algoritmos son útiles para cada una de las necesidades mas comunes.

von Luis H

24. Juni 2019

Las explicaciones en los videos son bastante buenas, aunque las actividades no permiten comprender del todo lo que se debe realizar para el examen final, cuesta mucho trabajo desarrollar la última entrega.

von Surya P S e

26. Juli 2020

The course was very concise and very helpful for people who want to learn ML for a career. It would have been even better if there were some OPTIONAL readings so that we can also learn the theory part.

von Sriram S

4. Juli 2020

This course is suitable for beginners. One can get hand-on experience on creating machine learning model and basic working knowledge of some classical machine learning algorithms. Overall, good course.

von Sudipan B

23. Mai 2020

A very good and informative one comes with online lab service. But the price for earning a certificate in this course is bit high that's why i'm giving it a 4 star. But the overall experience is 4.5/5.

von Shiva p

26. Juli 2020

Great course! One idea for improvement > Some of the comments in the Clustering and Recommender systems labs are hard to understand. Maybe you can rephrase / add more text to make it more intuitive.