Fake News Detection with Machine Learning

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In this Guided Project, you will:

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
BeginnerBeginner
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

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.

Skills you will develop

Python ProgrammingMachine LearningNatural Language ProcessingArtificial Intelligence(AI)

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  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

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

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