Sayak Paul

News Media Corp needs to be quick if they want to get ahead of their competitors. Their current news frontpage is put together manually, in a time consuming process where human editors create flashcards that summarize articles. It’s too slow—so senior management wants to supercharge the process using natural language processing. To get this built, they’ve turned to you. Your challenge in this liveProject is to create an NLP model that can reduce turnaround time for news editors with an automatic text summarizer. To do this, you’ll need to prepare and process your dataset with tokenization and padding, extract meaningful statistics from it, and finally use your dataset to train a deep learning model that can speedily summarize a body text.

In this liveProject, you’ll take on the role of a data scientist employed by the cybersecurity manager of a large organization. Recently, your colleagues have received multiple fake emails containing links to phishing websites. Phishing attacks are one of the most common—and most effective—online security threats, and your manager is worried that passwords or other information will be given to an attacker. You have been assigned the task of creating a machine learning model that can detect whether a linked website is a phishing site. Your challenges will include loading and understanding a tabular dataset, cleaning your dataset, and building a logistic regression model.