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

4.7
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12,619 Bewertungen
2,190 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.

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1576 - 1600 von 2,186 Bewertungen für Maschinelles Lernen mit Python

von Daniel D

26. Mai 2020

This is my favorite course in The Data Science Professional Certificate. Using real-world examples we implemented several ML models using scikit learn and python. There is also some exposure to numpy. This is a good course and overall provides applied data science methods with a comparison of common methods for classification.

von Collin C

15. Jan. 2020

Valuable material and well organized. There are many gaps in the explanations though. In the sample notebooks, there is a LOT of code that is not explained, so I have to Google the code or skip over it. The final tests a skill (transferring a machine learning model to an separate database) which was never taught or addressed.

von Voranipit C

9. Juli 2021

This course is great for concept of ML good enough for applying but not the best for who try to understand under the hood of ML

It's can go future if you need to know more math behind ML you need to take another course

scope of this course is too small you need more to learn about ML but this course is good to start with.

von Sascha B

21. Juli 2020

I think the course structure is great and provides a good overview of the various machine learning algorithms. In my opinion the coding excercises could dig a little deeper into the subject matter and sometimes a little more detail on the maths behind the algorithms would be beneficial. Overall it is a good introduction.

von Mišo D

15. Jan. 2021

Although a great course some of the materials are outdated. Some codes did not work without importing proper libraries/modules, needed time to figure out. The Watson Studio/IBM cloud looks different now than in the video in the course, so, it takes more time to figure it out.

In summary: Great, but needs an update.

von Folorunsho E

11. Sep. 2019

I had an amazing learning experience in this course. Although, i had challenges understanding some parts of the code, i found that i was able to scale through the capstone project without much stress. To further improve on the experience, it will be nice if some strange codes are properly explained and documented.

von Ruben G

23. Dez. 2020

Great course!

Just a short notice about the final exercise. It would be helpful to guide the students a bit further. I didn't know what to do with so many "blank lines" to fill in. In my opinion, you should whether explain what to do in each line or just leave a "big blank line" where we can write our scripts.

von Saadia H

7. Apr. 2020

I liked the course but felt that a beginner would not be able to cope up with the speed. However, if someone already has a basic knowledge of data analysis using python, this course would be perfect. I especially liked how each algorithm was explained in detail, how it works and what parameters effects them.

von Akil

8. Aug. 2020

The courses prior to this course in IBM Data Science for Professional were simpler, and the codes were easier to understand. Some of the codes in the labs session for this course were difficult to understand. However, overall the course was a lot more effective and more in depth.

Thank you for this service.

von Carol L

10. Mai 2020

Creo que este curso es muy avanzado para mi. Me gustó porque el instructor explicó muy bien de donde viene y como funciona las distintas metodologias, pero a nivel de practica algunas cosas no fueron explicadas y en el projecto final fue un poco confuso iniciar aunque realmente era aplicar las practicas.

von Deleted A

1. Feb. 2022

I loved this course "Machine Learning with Python" because it's a good courses, easy to understand , good explanation, and this course have an application with python programming language directly. So, you can understand not just the theory but how to code the theory that have been taught using python.

von PRATEEK S

16. Juli 2020

Material was great. Tutorial videos were great. The only improvement I would expect is on the Labs. Not only the lab environment was slow but there were certain errors in the questionnaire for final assignment for quite some time which no one seems to correct. If this is rectified, its a great course.

von Utku Ü

10. Okt. 2020

It's a good course to have some idea about ML case. A good part of the course is that, it has some brief expenations people to let them work further. Maybe not a learning course but a very good start. I do appreciate the instructors capacity to give lecture and summirize. Thank's for all the afford!

von Frank O

2. Sep. 2020

Loved the content, there needs to be more explanation about what Python code is necessary to complete the final project. In many cases I had to convert strings to integers and use plotting codes outside of examples. Please provide references to use that are necessary to complete the final project.

von Shiyang Z

3. Juni 2021

Great course for machine learning beginers. Helped me gain some general ideas about machine learning, and I'm able to conduct simple machine learning algorithm on my own after the class. Would be better if the python code has more detailed explaination, or has videos walking us through the code.

von Subikesh P S

4. Feb. 2020

The course thoroughly teaches about all the mathematical formulas and theoretical explanation in creating and predicting of data in data models. But I think if he also teaches about various python modules used for data science too, then it would have been much easier to do the assignments.

von Daniel G

26. Feb. 2020

The content is good and broad, although a little too superficial sometimes. It also provides a fair Python practice. My biggest complaint are the quizzes, that are full of bugs. Those bugs are the number one complaint in the forum, but there is very little responde from the management.

von Rafael S

20. Juli 2020

This is a really good course, where the instructor is clear, detailed when needed and practical in his examples. The only downsight is that this, as every other course of IBM Data Science program, is not designed to be a part of a full course: Often it repeats some previous concepts.

von OLENA S

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.