Detect Fake News in Python with Tensorflow

提供:
Coursera Project Network
このガイド付きプロジェクトでは、次のことを行います。

Collect and prepare text-based training and validation data for classifying text

Perform term frequency–inverse document frequency vectorization on text samples to determine similarity between texts for classification

Train a Deep Neural Network to classify Fake News articles

Clock2 hours
Intermediate中級
Cloudダウンロード不要
Video分割画面ビデオ
Comment Dots英語
Laptopデスクトップのみ

"Fake News" is a word used to mean different things to different people. At its heart, we define "fake news" as any news stories which are false: the article itself is fabricated without verifiable evidence, citations or quotations. Often these stories may be lies and propaganda that is deliberately intended to confuse the viewer, or may be characterized as "click-bait" written for monetary incentives (the writer profits on the number of people who click on the story). In recent years, fake news stories have proliferated via social media, partially because they are so readily and widely spread online. Worse yet, Artificial Intelligence and natural language processing, or NLP, technology is ushering in an era of artificially-generated fake news. Both types of fake news are detectable with the use of NLP and deep learning. In this project, you will learn multiple computational methods of identifying and classifying Fake News. 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.

あなたが開発するスキル

  • Tensorflow
  • Python Programming
  • Natural Language Processing
  • Fake News Detection

ステップバイステップで学習します

ワークエリアを使用した分割画面で再生するビデオでは、講師がこれらの手順を説明します。

  1. Introduction to Fake News and it's Effects on Society

  2. Collecting and Preparing Data for Text Classification

  3. Comparing Text with TF-IDF Vectorization

  4. Source Checking and Claim Matching

  5. Deep Learning Detection with Tensorflow

ガイド付きプロジェクトの仕組み

ワークスペースは、ブラウザに完全にロードされたクラウドデスクトップですので、ダウンロードは不要です

分割画面のビデオで、講師が手順ごとにガイドします

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