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Voltar para Perform Sentiment Analysis with scikit-learn

Comentários e feedback de alunos de Perform Sentiment Analysis with scikit-learn da instituição Coursera Project Network

4.5
estrelas
373 classificações
57 avaliações

Sobre o curso

In this project-based course, you will learn the fundamentals of sentiment analysis, and build a logistic regression model to classify movie reviews as either positive or negative. We will use the popular IMDB data set. Our goal is to use a simple logistic regression estimator from scikit-learn for document classification. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions....

Melhores avaliações

JQ

Jul 02, 2020

This project is very useful for people that don't know anything about sentiment analysis and it's approach with Scikitlearn, like me. It's very introductory.

AY

May 20, 2020

Very well designed course. Starting from the beginning of text pre-processing till evaluation of model, all steps are explained and implemented very well.

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26 — 50 de 56 Avaliações para o Perform Sentiment Analysis with scikit-learn

por Md. M H 1

Jul 17, 2020

nice course

por Suraj

Jun 10, 2020

thank you!

por Kamlesh C

Jun 27, 2020

Thank you

por Suraj Y

Jun 01, 2020

very good

por MD M A

Jun 21, 2020

goog

por Vajinepalli s s

Jun 20, 2020

nice

por tale p

Jun 16, 2020

good

por purnachand k

May 12, 2020

Good

por Tanzim M

Apr 09, 2020

nice

por Devsmita P

Aug 24, 2020

This course is Just fine and the required material to gain knowledge about hands-on Sentiment analysis as well as some machine learning models. I can across various models. The course also provides you websites related to some packages like NumPy, Matplotlib and Scikit-learn, you could 1st go through and then start the course for understanding it better. Hence, I would recommend others go through this course.

por Gopi K

Jun 04, 2020

Guided Project should be longer may be of 3-4 hours and consists of real world industry problem. It would be beneficial fo bachelors students.

por Dennis W

Aug 02, 2020

Easy to follow with simple instructions. Excellent introduction to text mining using TF-iDF and combine with simple machine learning.

por Justice A

May 27, 2020

This is a good project with well explained concept, it has help me remember things I have forgotten

por Aniket D

Jun 06, 2020

It was quite good and handy!

Just apt, and not much difficult!

I enjoyed learning it!

Thanks a lot!✌

por srinivas d

Jun 08, 2020

It would be good if we explain some terms in detail like tf-idf, count vectorizer, porter etc

por Bhanu T G

May 27, 2020

It'll be better if access time for cloud desktop is not limited.

por Sourav K

Jun 05, 2020

Offline work could be better than cloud desktop

por Manoj K B

May 15, 2020

buffers in the end modules

por K Y

May 29, 2020

Informative for beginners

por usha

May 18, 2020

Clear explanation.

por PUBALI M

May 05, 2020

nice

por Gurpreet S C

Apr 19, 2020

Good

por Brijesh G

Jun 10, 2020

More explanation is needed.

Pre-requisites were not mentioned.

Explanations needs to be more cleared.

Project is costly if compared to the content. Youtube has same content in free.

por Malki W

Jun 02, 2020

The practical session wasn't available. Forever connecting. But the videos are good. And glad the instructor has uploaded the notebook in resources

por Cesar K K

Jul 17, 2020

As a project, I was expecting a practical use. It looks like a simple exercise