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学生对 Coursera Project Network 提供的 Fake News Detection with Machine Learning 的评价和反馈

4.6
202 个评分
34 条评论

课程概述

In this hands-on project, we will train a Bidirectional Neural Network and LSTM based deep learning model to detect fake news from a given news corpus. This project could be practically used by any media company to automatically predict whether the circulating news is fake or not. The process could be done automatically without having humans manually review thousands of news related articles. Note: 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....

热门审阅

SG
Oct 23, 2020

Instructor Ryan has taken a lot of efforts to explain the topics, Advanced concepts like RNNs and LSTMs are clearly explained. Loved it.

GM
Aug 31, 2020

Each thing explains in a very simple way. As mention beginner to intermediate level.

筛选依据:

1 - Fake News Detection with Machine Learning 的 25 个评论(共 34 个)

创建者 Asif-Uz-Zaman K S

Aug 3, 2020

Did not explain to me that much. There was codes but without the basic knowledge of programming it is very difficult.

创建者 Rayna V

Nov 9, 2020

instructions are good. but could not download dataset and code. would've been better if that was easily available.

创建者 Joe H

Apr 10, 2021

Great course! Very concise and provides everything needed to complete an LSTM model for fake news detection. Instructor does a great job of explaining concepts and guiding users through all the necessary coding. Some coding skills required but not much. Compute time for model training is very long if using external Jupyter Notebook to follow along.

创建者 Cathreen S P

Oct 9, 2020

Great course, not that long but probably you will learn something extraordinary. As a Journalism graduate it is quite unique to stumble into this course since Fake news nowadays are the new thread you need to be skeptical when it comes to fact checking.

创建者 Sanket G

Oct 24, 2020

Instructor Ryan has taken a lot of efforts to explain the topics, Advanced concepts like RNNs and LSTMs are clearly explained. Loved it.

创建者 Manish A

Sep 17, 2020

Very well explained.

创建者 Isabelle T

Sep 27, 2020

This is a follow along project. The instructor goes through the code, which is explained well. He doesn't go over the math so much. This project is enough to go and read into it further yourself and practice on your own projects.

创建者 Nandan P

Apr 12, 2021

Its an Awesome Guided Project if you really want to understand about basic machine learning project and also about Fake News Detection working. Also How RNN and LSTM differ is somewhat explained in this project.

创建者 Murtuza B

Oct 4, 2020

Thank you so much sir for creating the course. really enjoyed your insights and the explanations were very crisp and clear. Looking forward to get enrolled in one of the other courses or projects offered by you.

创建者 Ganesh s m

Sep 1, 2020

Each thing explains in a very simple way. As mention beginner to intermediate level.

创建者 Keith P

Oct 27, 2020

Excellent introduction for AI/ML tools in the detection and analysis of fake news.

创建者 Harsh K M

Nov 3, 2020

This guided project is good for practicing the theory involved in NLP and RNNs.

创建者 Zakhar N

May 4, 2021

Very useful project, the instructor is very clear in presenting information

创建者 Bruce B

Sep 18, 2020

Great project, very approachable. Touches on all the essentials!

创建者 Mani K

Aug 14, 2020

Great practice for important concepts in data science.

创建者 Sidharta P

Sep 30, 2020

Amazing Course ! Expand my knowledge about NLP

创建者 Fausto B D S T

Jul 2, 2021

Really fun project with amazing instructor.

创建者 K P H

Sep 15, 2020

It Provided Simple and easy explaination

创建者 Hammad Y

Mar 15, 2021

Great Course but advance course

创建者 Ashok K

Aug 11, 2020

Excellent hands on practicals

创建者 K V S 1

Jun 27, 2021

great explanation sir

创建者 Virendra G

Nov 1, 2020

Excellent course.

创建者 Charudatt M

Jul 5, 2021

Best course

创建者 Stud 2

Aug 17, 2020

very useful

创建者 Paindla N r

Nov 22, 2020

very good