Chevron Left
Вернуться к Машинное обучение с использованием Python

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

Оценки: 5,608
Рецензии: 730

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


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.


Aug 04, 2019

The instructor was awesome. His voice was crisp and to the point. The course is actually well laid out with proper structure. Altogether a great learning experience. Cheers... Keep up the good work.

Фильтр по:

51–75 из 727 отзывов о курсе Машинное обучение с использованием Python

автор: Wagner M

Nov 04, 2019

[PT-br] Um dos melhores cursos onde se alinha teoria com a prática na área. Conteúdo bem completo, passando pelas diversas técnicas de ML, com vídeos muito bem explicados e conteúdos práticos que demonstram como aplicar cada técnica. Além disso, as provas são bem desafiadoras e o projeto final é bem completo, o que aumenta o valor do certificado ao final.

автор: Arindam G

Dec 20, 2018

No Doubt COURSERA is always best AND MNC like IBM,Google courses associated with coursera are MIND-BLOWING.

The Instructors are so great at Explanation Part that hardly anyone won't Understand All the Topics

I would love to thank all the INSTRUCTORS who created such a Awesome Content for us.

My Personal Ratings For All the Instructors: 100 / 100

автор: Lior B

Aug 28, 2019

Great introductory course. Clear explanations and good homework to get your hands dirty and see results of algorithms.

A rather minimal mathematical understanding is assumed by the course so begginers would not be overwhelmed.

Keep in mind this course will not make you an expert or teach you how to write some of the more advanced algorithms by yourself.

автор: Vijayanandhan

Jul 05, 2019

The Video content is very clear and simple to understand the concepts and lab is very good. The IBM Trainer


did a great job in this course. Got hand on experience on Machine learning. Final Project is helpful to apply all the concepts I learned throughout the course. Glad to get this certification from Coursera and IBM.

автор: Rakshith K H K

Dec 27, 2019

under well designed syllabus , became easy to learn and solve real world examples,which keeps motivated through out the learning process . The fascinating about this platform is the ease for access to quality resources or otherwise it is difficult. The end of course meant to me s skill for solution to many issues irrespective of field.

автор: Azam D D

May 28, 2019

This is an excellent course for a quick review of what you know about Machine Learning.

I think you should mostly know about the basics of programming in python and also Machine Learning, but this course gives you a great quick review and also is an excellent example of python machine learning tools.

I strongly recommend this course.

автор: Luca A

Jun 05, 2019

A nice and quick overview of how the main machine learning methods work and how to apply them by means of the python library Scikit Learn. It does not dive too much into the details, but explains the main ideas clearly and provides you with the main python routines to use ML on real data.

Recommended if you are new to the field!

автор: NASIR A

Feb 27, 2019

This course is one of the best course i have taken on coursera, it not only treats you as a beginner but also provides the detail technical details so that one can learn more on his on. The lectures are clear and quiz are tough, Labs give a thorough overview of each topic. I would like to thank you the instructor for his effort.

автор: Daniel K

Nov 01, 2019

The information in this course is laid out in a easily digestible format that makes it possible to fully own the knowledge that you gain and put it to the test. I appreciate that the videos are straight to the point and that the jupyter notebooks illustrate varying techniques for cleaning data. Tremendous value.

автор: Amy P

Jul 25, 2019

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: V M R

May 18, 2019

Complex concepts of machine learning algorithms are explained clearly with an illustration. Learner definitely have confidence in Machine learning after this course completion. A practical assignment work is really helped the learner to do the implementation of classifier model of their own and gain confidence.

автор: Shakshi N

Jan 15, 2020

This course has been awesome. I have been doing ML Work for my college for quite some time, but never understood what goes in it, and kind of surfed through the net and just did the work. But this course has given me in depth knowledge of the logic that goes behind these algorithms and for that I am very glad.

автор: Mayank P

May 21, 2019

This course offers a simple and effective experience. I learnt how to find the most accurate algorithms in the scenarios. Most importantly, the Jupyter notebooks provided are although optional, but you should study them thoroughly. They might seem difficult on an overview, but are very easy to understand.

автор: Surendrabikram T

Jul 13, 2019

Great course.

It could be even better if programming assignment were provided in each week but still, final assignment was of great quality and I found it really engaging. The program introduces you to scikit learn which is again a wonderful advantage of taking this program. I am giving this course 5/5.

автор: Luis M

Jan 08, 2020

The course was thorough and a great introduction to machine learning. The capstone project was challenging and required me to have a good working knowledge of the various models. This has been the most intensive course, so far (course 8 of 9), in the IBM Data Science Professional Certificate.

автор: Priyansh S

Jul 20, 2019

The course is really good for machine learning beginners. I would recommend everyone to take this course as it gives you all the basic knowledge and working of ML. It is fun to do with the Jupyter notebook tool which gives a great actual experience. Thanks a lot. This course helped me a lot.

автор: Jeffrey P

Jun 17, 2019

I think it would be beneficial to talk about neural networks somewhere after the gradient of steepest descent section. I did appreciate the course talked about many other ML algorithms that are not typically covered by other programs - and the lab notebooks are extremely valuable.

автор: Yi Y

Oct 03, 2018

It is one of the best introduction course to Machine Learning.

The material is well explained to someone with a beginner level of understanding to Statistics and Machine Learning.

All the material is presented in a way that is easy to understand, without leaving out the details.

автор: Omri

Nov 27, 2019

Great course, cover many important aspects of classical machine learning algorithms. The lectures are very focused and not tedious. Labs are excellent, and can serve as a starting point for every data science project in the future. I definitely recommend taking the course.

автор: Toan T L

Oct 28, 2018

Great course.

Knowledge wise, just like Prof. Ng's, minus the mathematics foundation.

Practical wise, carefully designed labs really help learners understand the data cleaning processes, understanding data through visualization, ML algorithms and evaluation metrics.

автор: Ashish S

Sep 14, 2019

Can include more details. Every time I was more interested in a certain topic it mentioned it was out of the scope of this which was disappointing. I absolutely loved the teaching and would like to hear more to his lectures and sessions!

Amazing course. Thank you!

автор: Patrick W B

Jan 19, 2020

The lessons are very simple to understand with both logic intuition and mathematical explanations.

It is really the best course and beginner friendly.

I strongly recommend this course for anyone willing to start a career in Artificial Intelligence technologies.

автор: Victor A M B

Sep 17, 2019

Muy buen curso, se da información bastante relevante acerca de los algoritmos de Maching Learning. Debe tenerse en cuenta que después de este curso será necesario profundizar más en los diferentes algoritmos, pero en mi opinión esto se dará con mayor facilidad.

автор: henry c

Sep 19, 2019

The course is not easy, but it is a lot of work with dedication and commitment to the management of platforms, if the concepts are very understandable and the laboratories are very didactic, thank you very much for sharing the information and thanks again ...

автор: Moez B

Feb 25, 2019

Excellent course on machine learning principles and various algorithms. It's a great start if you want to jump into the practical side of implementing ML using Python libraries, without getting too deep into the theory behind the most popular algorithms.