ProjectBeta
Fake News Detection
Challenging
4 completions
~ 38 hours
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Your end product will be able to classify a given news article as either fake or real. While working on the project, you will discover different ways to numerically represent textual data (bag-of-words, TF-IDF, and contextual embeddings) and various ML models such as Naive Bayes classifier and the Long Short-Term Memory network.
Provided by
JetBrains Academy
About
Disinformation in the media is a significant problem in today's world. Therefore, the automatic detection of fake news is a topic that is being widely studied. In this project, you will create your fake news classifier using the latest machine learning and NLP techniques.
Training project
This project allows you to practice and strengthen your coding skills, helping you get ready for more advanced tasks ahead.
What you'll learn
Once you choose a project, we'll provide you with a study plan that includes all the necessary topics from your course to get it built. Here’s what awaits you:
Build a Multinomial Naive Bayes text classifier to detect fake news.
Discover how other machine learning models perform on fake news detection, train a State Vector classifier and a Logistic Regression model, and compare their performance.
Build a mega-model that performs majority voting between the three types of machine learning models.
Time for some Deep Learning. Train an LSTM model on the fake news data.
Reviews
2 years ago
I have learned to use different Vectorization techniques and model my data using different algorithms like Naive Bayes, Logistic Regression, and SVC, then I used advanced deep learning techniques using tensorflow to create a deep model.
5.0
Learners who completed this project within the Data Scientist course rated it as follows: