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Вернуться к Машинное обучение с использованием Python

Отзывы учащихся о курсе Машинное обучение с использованием Python от партнера IBM

4.7
звезд
Оценки: 9,287
Рецензии: 1,496

О курсе

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....

Лучшие рецензии

RC

Feb 07, 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.

RN

May 26, 2020

Labs were incredibly useful as a practical learning tool which therefore helped in the final assignment! I wouldn't have done well in the final assignment without it together with the lecture videos!

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51–75 из 1,484 отзывов о курсе Машинное обучение с использованием Python

автор: Sylvio R

Mar 03, 2020

O curso em si é bom, mas como a maioria dos cursos online não temos espaço para dúvidas (e não, o fórum não é suficiente).

A tarefa final é muito mal explicada.

Também senti falta de mais Python durante as aulas, que só cobrem o aspecto teórico. Embora muito bom, ao se deparar com o código, surgem muitas dúvidas.

автор: Dhruv K

Apr 11, 2020

Could have explained a little coding in videos instead of putting it in labs...

автор: Parth R J

Mar 03, 2019

very bad course

no proper instructions or explanations in videos

автор: Serdar M

Dec 10, 2018

labs are not easy to understand

автор: Anton M

Apr 28, 2020

A bit dissapointed by this course. The main topics were given clear and simple, but there were too few details, saying that all the details are out of scope of the course. But I would prefer to have more information and also more mathematical details (I find the argument that it needs appropriate background strange: if one wants to learn Machine Learning, should already have some basic mathematical background as knowledge of derivatives, integrals, etc).

Another big disappointment was absence of the graded programming assignments, except the final project. Every part of the course had just graded Quiz, but real hand-on scripting in python was given just as non-graded example, and then final assignment basically consisted from the same code.I find this approach quite useless. Also the final assignment had to be done at the IBM Watson website - I guess just for advertisement of IBM services - but this is useless to waste time on registering there, and figuring out how to do things there, if instead could be done inside coursera itself.

And finally, there few some mistakes and typos e.g. in the final assignment, which made everything a bit confusing.

автор: Joe R

May 26, 2020

This course was taught nowhere near as well as the other courses in this certificate track. The code syntax was not explained well at all and it took forever to decipher. The lectures were also not very informative. I would have appreciated a much more in-depth look at the concepts or at least explaining them in further detail. These courses are supposedly for "beginners" but there is no way a "beginner" would be able to get through a course like this without explaining everything better.

The final assignment was also VERY confusing. I would recommend the instructors revisit and revise the course material to make it more engaging and do a better job of explaining the concepts.

автор: Oliver S

Apr 25, 2020

I liked the videos, but there are a lot of mistakes in the notebooks, especially in the solution for the final assignment (which results in unfair gradings). Most of them were mentioned in the forums months ago, but as with all IBM courses, that I have finished so far, no employee seems to care. None of the mistakes gets corrected, and most of the time, you don't even get a reply from one of the moderators.

автор: Gilbert V

Feb 07, 2020

Course is largely a scam. At the end you have to have a peer reviewed project that will prevent you from finishing the course if other people do not grade your project. You can have a high enough overall grade that you could get a 0 on the final and still pass and still be out of luck if people decide to not help with grading, which is exactly what happened to me. Do not waste your time and money if you want to be at the mercy of other people.

автор: Aditya V R

Jun 30, 2020

Too much maths. Those who didnt have any background in math, it is very difficult for them to pass this course. They wont explain the code. They explain only the concept. The code is very difficult to understand. Very complex code. I many times thought of giving up. Finally completed with luck and hardwork.

автор: Pierre-Antoine M

Mar 02, 2020

That course is a joke.

Videos are less informative than wikipédia, hands-on labs have praticaly no exercises and are really shallows.

Finally the Peer-Graded Assignment is made even more difficult, because not having correct lessons and hands-on is not bad enough, by being really bad worded

автор: Ubaid M W

Oct 22, 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.

автор: Karol S

May 02, 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

автор: Joaquín R

Mar 17, 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.

автор: Oritseweyinmi H A

May 13, 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.

автор: Ugwu G C

May 14, 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.

автор: Kalpesh P

Nov 29, 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!

автор: akshay s

Aug 09, 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.

автор: Tushar S S

Jul 15, 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.

автор: Sri K P

Apr 14, 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

автор: Christopher S

Jan 14, 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.

автор: Iskandar M

May 06, 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!

автор: Peter P

May 20, 2020

This course was perfect, especially in my situation. I know all of the math behind neural networks, and fitting, but there were many algorithms I've never been exposed to - and this course exposed me to a lot! I liked the hands-on coding labs and learned where to find a lot of Python stuff that I wasn't aware of. A lot of terminology that I'd heard about is now clear in my mind. And the amount math was balanced perfectly with the getting things done.

автор: Peruru S S

Dec 11, 2019

I really enjoyed taking this course. The instructor is to the point, crystal clear. Nicely explains the essence of the topics in 5 to 6 minutes. I recommend this as a good introduction course to get a basic overview of different algorithms. However, if one wants a deeper understanding with specific details, this is not the course. This course will definitely serve as a good introduction which help us to get motivated to do more advanced courses.

автор: Ashit C

Jul 31, 2020

I really enjoyed during this course . Gives you a lot of skills of how to deal with data ,predictions or recommendations. At the end i know how day to day life works based on machine learning as they quite kept few real world examples while explaining. Little bit of difficulty i faced while doing main project as there was less guidance on what we have to show at the end of project. But it was a great course. Worth spending time over it.

автор: Clarence E Y

Apr 22, 2019

This course will challenge learners to commit to learning about the key objectives for using algorithmic approaches to answering important business questions using data. The lectures cover the theoretical foundations of the "relationship" algorithms used for classification and clustering methods. Additionally, the labs provide a fully integrated environment in which learners can do hands-on investigations to gain proficiency.