Tutorial: Fake News Detection with Machine Learning
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
In this guided tutorial, we will train a Bidirectional Neural Network and LSTM based deep learning model to detect fake news from a given news corpus. This tutorial 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
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:
Understand the Problem Statement and business case
Import libraries and datasets
Perform Exploratory Data Analysis
Perform Data Cleaning
Visualize the cleaned data
Prepare the data by tokenizing and padding
Understand the theory and intuition behind Recurrent Neural Networks
Understand the theory and intuition behind LSTM
Build and train the model
Assess trained model performance
How Guided Tutorials 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
Reviews
TOP REVIEWS FROM TUTORIAL: FAKE NEWS DETECTION WITH MACHINE LEARNING
Instructor Ryan has taken a lot of efforts to explain the topics, Advanced concepts like RNNs and LSTMs are clearly explained. Loved it.
Not that good for people new to python and ml, many high level concepts are used in this project!
Each thing explains in a very simple way. As mention beginner to intermediate level.
Excellent introduction for AI/ML tools in the detection and analysis of fake news.
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