Congratulations on choosing to participate in the Virtual Internship Program on AI Engineering with Open Weaver! This 4-week program is designed to be an interactive & practical internship and will help you gain industry-ready skills through project-based learning. The internship will consist of 3 Bootcamps (1 per week), a few coding exercises, a project, and a final assessment.
WEEK 2 - AI ENGINEERING
Today, 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 today’s Bootcamp and this week’s exercises, 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.
Learning Objectives
After completing this course 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.
10-Min Tutorial
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 tutorial 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. Revisit the concepts discussed during the live bootcamp session in this 10-min tutorial video. If you would like to watch the recording of the entire bootcamp please click HERE.
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.
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.
Week 2 Coding Exercises
Complete interesting coding exercises and receive your second level badge for this internship! Submit your solutions by adding a screenshot of the code and the output in the form.
It is mandatory for you to complete your weekly Coding Exercises in order to receive your Internship Completion Certificate.
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 NL 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 .
Support
Reach out to us by clicking on the reply button below for any help you may need with this course. You may also use the chat feature for support. To access the reply and chat feature, please sign-in to the the Community.
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