AI fake news detector helps detect fake news through binary classification techniques and helps build better experiences by controlling the flow of disinformation in politics, businesses, climate change, and more. It’s built on top of various powerful machine learning libraries. The tool works by training a neural network to spot fake articles based on their text content.
After completing this course you will:
- Have a good working knowledge of the Fundamentals of Natural Language Processing.
- Learn various concepts involved in building the fake news detector such as data analysis, text mining, calculating sentence embeddings and computing sentence similarity.
- Have a fully functional fake news detector prototype that you can customize, showcase, share, using the existing source code.
- Be able to fine tune the model to enhance its performance.
Complete this course in 3 easy steps to earn your certificate!
STEP 1 : Watch the below self-guided tutorial.
STEP 2 : Practice as you watch the video by installing and working with the kandi 1-click solution kit.
STEP 3 : Complete the assessment to receive your certificate.
STEP 1 : TUTORIAL
Watch this self-guided tutorial on how you can use training data, NLP pipeline, TF-IDF vectorizer, and text classifier to build your own AI fake news detector.
STEP 2 : PRACTICAL EXERCISE
Click the below button to access the fake news detector kandi kit. This kit has all the required dependencies and resources you need to build your application.
kandi 1click kits include Python, Jupyter notebook and helps you learn to apply the concepts of machine learning, natural language processing (NLP) to build your own Fake New Detector, learn to use a vectorizer technique to transform text into a meaningful representation of numbers and use supervised learning algorithms like Naive Bayes, Logistic RegresssionCV to build a model to detect fake news.
Click on the 1-Click Installer button on the kandi kit page to install the fake news detector kit. On installing and running this kit, you will have a working model that you can customize and use in your project.
Code Snippet Exercises
Below are three coding exercises that will help you advance in your journey in Fake News Detector Using Natural Language Processing(NLP). To get started, use the relevant keywords to search for simple code snippets in the search bar on kandi.
Exercise 1 - Spam filtering: remove Stopwords: This exercise shows how you can filter stopwords, an essential step in NL pipeline.
Exercise 2 - Lemmatization using SpaCy: Try this exercise to get to the root form of the word using the Spacy library.
Exercise 3 - create word cloud from csv: Learn to build a very simple word cloud using Python using only a few lines of code to create a visual representation (image) of word data .
After completing this step, proceed to STEP 3.
STEP 3 : ASSESSMENT
Complete a short assessment and earn your certificate now.
Your assessment will be reviewed and you will receive a verified certificate via email within a week.
Reach out to us by replying below for any help you may need with this course.
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