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

13,600 Bewertungen

Über den Kurs

Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning. This course will begin with a gentle introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning, linear & non-linear regression, simple regression and more. You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You’ll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN. With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project you will demonstrate your skills by building, evaluating and comparing several Machine Learning models using different algorithms. By the end of this course, you will have job ready skills to add to your resume and a certificate in machine learning to prove your competency....



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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1751 - 1775 von 2,369 Bewertungen für Maschinelles Lernen mit Python


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 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.