In week 2 of the internship, our focus will be on how 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. Through the Week 2 Live Bootcamp and Coding Project, we will learn how to apply Machine Learning & NLP techniques for building a Fake News Detector Engine using Training data, NLP pipeline, TF-IDF vectorizer, and Text classifier.
After completing this week’s Bootcamp & Coding Project you will:
- Have a good working knowledge of the Fundamentals of Natural Language Processing(NLP).
- 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 and fine tune the model to enhance its performance.
AI Fake News Detector is 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. When you run your own data through the tool, it gives you back a list of articles it thinks are likely fake.
Watch this Live Bootcamp (recording available below after live session) on building your own Fake News Detector Engine & learn how to train the model, and use supervised learning algorithms like Naive Bayes, and Logistic RegressionCV.
NOTE :: You must sign in/sign up (top right corner of this page) to record your attendance while watching this live or recorded bootcamp. Just sign in and then watch - it’s as easy as that!
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.
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.
Below are three sample 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 .
Sample Exercise 1 - Spam filtering: remove Stopwords: This exercise shows how you can filter stopwords, an essential step in NLP pipeline.
Sample Exercise 2 - Lemmatization using SpaCy: Try this exercise to get to the root form of the word using the Spacy library.
Sample 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.
Complete and submit this interesting Coding Project and receive your AI Engineering Learner Badge!
It is mandatory for you to complete your weekly Coding Project in order to receive your final Internship Completion Certificate.
NOTE :: For questions 6-10, please ensure you upload screenshots of your code and outcome in PNG format ONLY. We will not be able to consider answers provided in text or any other format. You will receive your Badge #4 if you meet the minimum requirement of 60% correct answers. If not, you will receive an email from us requesting you to retake the Coding Project.
Showcase your progress & win exciting prizes!
- Share your badge on social media. You can save the image and share.
- Add a short note on why you are excited to learn new digital skills.
- Ensure you tag #Ready2GoDigital @ICTAcademy @OpenWeaver.
Win goodies every week! Share badges every week and stand to win Airpods at the end of the internship!
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