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

4.7
Sterne
13,621 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....

Top-Bewertungen

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.

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.

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76 - 100 von 2,376 Bewertungen für Maschinelles Lernen mit Python

von Aitekenov S

30. Aug. 2022

Whoever is from the CIS countries beware of IBM's faulty practices.

It contains tons of marketing for IBM products. Without those products you won't finish many assignments. Moreover, IBM blocked my country from the ability to create an account on their services. So, I can not even finish those courses.

von ubaid m w

22. Okt. 2018

In lab there are many funtion , libiraries Which have been used first time with out any description , then I have to search for each and every funtion or lib which is way time consuming which make this course worst courses in my list.

von Nishan P

5. Nov. 2020

Instructor are going to fast. They are literally reading the slides without proper implementation of the ideas and algorithm explained. Even I can do that, absolute waste of money

von Karol S

2. Mai 2020

wrong grading on quizes (multiple choice questions which are graded 0 or 1), not clear instructions, who write this course? One of the worst courses i took in years

von Joaquín R

17. März 2020

The course was going well with the videos and labs, until the capstone peer-reviewed area. Disastrous instructions, poor supervision and assistance. I am appalled.

von YUN H

16. März 2020

Insufficient explanation, bad lab experience, and the final assignment was a nightmare.

Video is short, so you got to figure out things by yourself.

von Luiz P F

17. Okt. 2020

Videos and assignments are very repetitive. It induces students to copy dull code rather than think about solutions

von Kshitij K

16. Aug. 2020

Everything taught int his course ends with a line "unfortunately it is out of the scope of this course"

von Syed A

12. Mai 2020

outdated notebooks, had to google everything anyway

von Tummala. L S s

25. Nov. 2021

we are not able to get ceritficate

von Oritseweyinmi H A

13. Mai 2020

Great course! Get ready to learn, code, debug, sweat, learn some more, fix your code, then finally smile when your ML models work smoothly.

That last statement described my workflow during the final assignment/project of this course.

Quite simply, this course was brilliant because not only did it bring everything we've learned so far together but it also built upon the last course and properly introduced us to Machine Learning and its applications. In his videos, Saeed successfully breaks down complex topics into digestible byte-sized content and ensures that you intuitively understand what is going on.

One of the best pieces of advice I have received in regards to my learning and in life in general is to make sure you have a strong grasp of the fundamentals and these become building blocks to much more complex topics. That in a nutshell is what I believe this course has done for me.

To those who are reading this review, trying to decide whether or not to take this course... just do it! What are you waiting for? No seriously? This might be one of the best decisions you make this year.

If you've been racing through the other courses up to this point, I advise you to slow down once you get here and really try to digest what Saeed has taught here.

Watch the videos, pause, take notes, rewind, continue watching, learn, code. Iterate.

von George U

14. Mai 2020

I love every bit of this course. It is very informative and the explanation by the instructor is second to none. He explained most of the concepts especially using real life scenarios like customer segmentation, detection of cancer and many more. Using these real life examples in the explanation made me understand the course very well and also appreciate machine learning. It will be very easy with anyone with mathematical background though people that are not mathematical inclined may have some difficulties understanding some of the concepts. Nevertheless, going through the lab section will make you understand the concepts very well even if you didn't get all the theoretical concepts. The final project was also centered based on what was taught and easy to follow by anyone that paid apt attention to the lectures and followed duly in the lab exercises. Kudos to the instructor.

von Alpesh G

25. Aug. 2021

The course start with introduction to Machine Learning, with various industrial examples and applications along with libraries used for Machine Learning. Understood how supervised machine learning is different from unsupervised machine learning. Then learnt the concept of Linear, Non-linear, Simple and Multiple regression, and their applications, also how to evaluate your regression model, and calculate its accuracy.  

Practiced with different classification algorithms, such as KNN, Decision Trees, Logistic Regression and SVM. Introduced with main idea behind recommendation engines, then understood two main types of recommendation engines, namely, content-based and collaborative filtering. The course ends with Peer Graded Assignment to apply all the ML modeling learned.

Thanks to IBM and Coursera for this great learning experience.

von Kalpesh P

29. Nov. 2019

I personally felt, it is one of the best modules offered as part of certification program. Data science has large number of algorithms, so naturally it is difficult to cover most of them and more importantly it is difficult to decide where to start from. Module is well designed, and it has provided basic to intermediate knowledge of most of machine learning algorithms, must to know for beginners. Few minutes introductory video on any given algorithm, followed an hour-long lab practice is really helped to understand algorithm and it’s implementation using python. Provided structured course really helped me to perform machine learning implementation using python. Great content to spent time on!

von Abid R

1. Jan. 2021

The best way to succeed in this course is to when doing the labs, write down with "hand" every line of code on a separate place, though, you will not understand most of it, just keep going. And then type it on Jupyter notebook from "hand written notes". This process might seem hard effort or seems like no learning is there but trust me this process will get you break the thick wall of Machine Learning and python code. The rest will follow. After following the process, I feel very familiar with code, machine learning algorithms and terminologies which I guess is big achievement. I also believe ISLR can help later in understanding these algos and set up more solid foundation.

von Ahmed S

18. Okt. 2020

Certainly a great course, clear voice and visuals in which the concepts have been explained clearly with rich details. I have noticed many are complaining about the math, lab, coding and the conceptual explanations; so here is a reminder than the course strongly suggested a 'background in Python programing language' in the beginning. Additionally, this is an 'intermediate/ advanced' course for engineers and data scientists, so a well-established knowledge in math should've been already acquired by default, even though the math needed here is very basic and can be done automatically. Also, understanding the conceptual part is very important to perform tasks correctly.

von akshay s

9. Aug. 2019

I am thoroughly enjoying the course. The codes written are the shortest possible codes but the narrations are just fabulous to comprehend and remember. I need more practice to write the codes correctly by my own but my fundas are all cleared and I know exactly why am I doing the next step. Having worked my way through the IBM Data Science courses, this one was the "pay off" - it was so cool to finally apply more sophisticated techniques to real world data sets. The labs were fantastic. Highly recommend this course to anyone interested in learning about the most popular machine learning algorithms.

von Nima G M

4. Okt. 2020

This is a Perfect course, except for the name of the course. It is one of the perfect courses for those who wanted to become familiar with different machine learning algorithms (different classification algorithms, as well as different clustering algorithms). In fact, it is the course I definitely recommend for those who want to start machine learning. By the way, I did not understand why the author used this title for this awesome course, given that he is not used Python programming. The best title might be this one, I guess:

"Different machine learning problems, and algorithms "

von Eirwyn Z

22. März 2022

It's very basic but essential to understand more complex topics regarding machine learning. The Course is well-structured with good presentations. The only issue that I've encountered is with the IBM Watson Studio. For some reason, it just refuses to accept my credit card (which I'm currently using to pay for my other stuff) and boots me off the website every time. I create a GitHub repository for the final project and, fortunately, people have no problem reviewing my notebook on GitHub which allowed me to get around with the issue with IBM Watson Studio.

von Tushar S S

15. Juli 2020

This course is perfect for beginners. It gives a basic idea about clustering, regression, decision tree, recommender system, classification algorithms along with Labs. You should know a little bit about Python programming and few libraries like NumPy, pandas, sciPy, and sci-kit learn. The Labs are great because you will be using the concepts learnt in the video lectures on the sample datasets and when you see the results, it will motivate you to go for some hands-on projects from Coursera Rhyme Project Network and it will be beneficial for you.

von Sri K P

14. Apr. 2019

This course is an excellent platform to understand the basics of Machine Learning with python. The lab tools pioneer a way to understand the code and implement it. The videos are crisp and clearly mention the scope of the course which creates a curiosity to know more. However, the peer graded assignment is not an efficient way as 'sample notebook" paves the way to plagiarism. The peer grading also restricts the user creativity to write a simpler code as it may not be understood by other peers. Overall I am very happy with the course

von Christopher S

14. Jan. 2020

Excellent content and relatable use cases. As a beginner in data science with no formal programming, the information is presented in a way to help you understand the fundamentals and then apply them using the pre-built python packages that are widely available. I started with data science 3 years ago and it was very difficult to get started without any programming or statistics background. This course does a tremendous job of making it accessible, understandable and quite frankly a lot fun in the process.

von Aniket A

9. Okt. 2020

This course is fantastic, It has adequate amount of theory supplemented by labs. I also like the Watson Studio, and the fact that you actually learn to use some industry level tools in this course really takes the icing on top. The staff is supportive and wonderful, the community and cohorts are great. Overall I would happily recommend anyone who has absolutely no knowledge about Data Science to start right here with this course. Really enjoyed and thank you IBM for you digital badge. :-)

von Oleh L

20. Aug. 2020

Well structured course, which will give you understanding of the applied way of working. The topics are explained in quite enought details, allowing you to use learned approach in practical way.

What I would personally wish - a bit more examples of different kinds. It should not be included into main structure of the course (to decrease a work load of Instructors and Students). It needs to go into Optional part, but I'm sure - who is interested in, will finish the task.

von Iskandar M

6. Mai 2019

This course needs basic knowledge on algorithm and programming experience. I really recommend this machine learning course for those who have computer science, statistics, or math background. The instructor is very clear, concise, and using simple diction when explaining the subject. All presented in here is valuable and worth reading and listening. The final task is somewhat challenging, but we'll have to really dig into the examples presented in the labs. Thank you!