Fake News Detection with Machine Learning

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在此指導項目中,您將:

Create a pipeline to remove stop-words ,perform tokenization and padding.

Understand the theory and intuition behind Recurrent Neural Networks and LSTM

Train the deep learning model and assess its performance

Clock2 hours
Beginner初級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

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.

您要培養的技能

Python ProgrammingMachine LearningNatural Language ProcessingArtificial Intelligence(AI)

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  1. Understand the Problem Statement and business case 

  2. Import libraries and datasets

  3. Perform Exploratory Data Analysis

  4. Perform Data Cleaning

  5. Visualize the cleaned data

  6. Prepare the data by tokenizing and padding

  7. Understand the theory and intuition behind Recurrent Neural Networks

  8. Understand the theory and intuition behind LSTM

  9. Build and train the model

  10. Assess trained model performance

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

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