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Вернуться к Машинное обучение с использованием Python

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

Оценки: 8,989
Рецензии: 1,434

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.


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!

Фильтр по:

251–275 из 1,421 отзывов о курсе Машинное обучение с использованием Python

автор: Robert E W

Feb 07, 2020

An excellent course, covering multiple machine learning algorithms which allow you to compare and contrast their strengths and methodologies.

автор: Gil D L

Jun 18, 2020

The final assignment is a bit unclear with grading but overall great course. Learned a lot with the applications of python in data analysis.

автор: Aneta B

May 02, 2020

The course is an excellent introduction to Machine Learning. It is balanced in terms of theory and practice, and the workload is manageable.

автор: Philippe D

Oct 21, 2019

Very informative course. The lectures were very professional and the structure of the course and the notebooks were all very well presented.

автор: Amber P R

May 20, 2019

A good course designed nicely and recommended for beginners as it is very easy to understand and practical assignments are really effective.

автор: Gary T

May 06, 2019

This course changed my life and view about Python and Data Analytics. I wish everyone could take this course. Grateful to IBM and Coursera.

автор: Dhiraj K K

Oct 01, 2019

One of the best course for learning Machine Learning basics.

Amazing instructor, good syllabus and labs. With interesting final assignment.

автор: AVADH P

Jan 07, 2019

Very well for beginners. This course gives a great insight to the new learners on how to implement machine learning algorithms in Python.

автор: Anuar M

Mar 22, 2020

Excellent class for machine learning beginner. Lots of insight on the ML algorithms and what problem does the algorithms used to solve.

автор: Hernán C

Mar 03, 2020

El contenido del curso es muy didáctico, no es el primer curso que hago sobre machine learning y hasta ahora me pareció de los mejores.

автор: Mauricio R O

Apr 27, 2020

Excelente curso. Contribuye en gran medida al entendimiento de diferentes algoritmos de ML y por consiguiente algunos de sus alcances.

автор: RICARDO H R

Jul 04, 2020

I really liked this course, the videos are well done and the exercises perfect, it gives you the chance to go as deep as you want.

автор: Rohith R N

Apr 13, 2020

A very well structured course, the content covers all basic aspects of ML with good sample datasets. Thank you Joseph and Saeed !!

автор: vignaux

Mar 24, 2020

This course is excellent. It is a very good approach to ML with python without being a mathematician. So forward and well done IBM

автор: Oluwatade J A

Aug 02, 2019

Awesome experience and learning platform on learning machine learning algorithms, clustering techniques and recommendation systems

автор: Mustafa H K

May 25, 2019

Really well structured course with the right amount of guidance without excessive hand holding due to which the learning is real!

автор: Gopala R

Nov 19, 2018

Real informative and challenging! A lot of technical things taught and the final assignment was very interesting and challenging.

автор: Samuel A O T

Jun 28, 2020

It was an awesome experience on this machine learning platform. The experience gained is very useful for real world applications

автор: Shivam M

Dec 21, 2019

Awesome course to hands on learning of machine learning algorithms such as k-nearest neighbor, recommender systems for a layman.


Nov 04, 2019

The course was very explanatory and the professor was teaching in a very unique way. Really thankful to course era and the prof.

автор: Ravi S

May 09, 2020

I like the fact that there is ample amount of practical exercises that implies you are not going to forget these things easily.

автор: Kandasamy R

Apr 29, 2020

An engaging course that covers the core concepts across various areas in ML and provides the right level of hands-on exercises.

автор: Siba P C

Jul 13, 2019

Good course for getting started with basics. Carry on your self-study after this course to get even better at machine learning.

автор: sergey k

Feb 19, 2019

Dear all.

It would be grate if you will add roc-curver and ordinal regression examples into this corse.

Sincerely, Sergey Kutenko

автор: Israel S S

Sep 05, 2019

It was a complicated course, but I acquired very valuable practical knowledge about programming to implement Machine Learning.