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.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
liveProject mentor Lalitanand Surampudi shares what he likes about the Manning liveProject platform.
When you start your liveProject, you get full access to the following books for 90 days.
Sayak is employed at Carted where he works on representation learning from product webpages, as well as other NLP use cases. His interests are in the general representation learning area and include self-supervision, semi-supervision, multimodal alignment, and model robustness
This liveProject is designed for developers interested in data science and for beginner data scientists. To begin this liveProject, you will need to be familiar with:
Basics of Python and its utility functions
Basics of pandas
Basics of NumPy
Basics of scikit-learn
Basics of data science
you will learn
In this liveProject, you’ll learn to build a machine learning model using common Python libraries. You’ll develop techniques for querying datasets, data cleaning, performing hyperparameter tuning, and analyzing and summarizing the performance of your models. These skills can easily be applied to a wide variety of machine learning tasks and other data projects.
Loading and understanding tabular datasets using pandas
Preprocessing tabular datasets with NumPy
Preparing reports on your data with visualization tools
Creating a logistic regression classifier as a baseline model using scikit-learn
Using random searching to find optimal hyperparameters of the baseline model
You choose the schedule and decide how much time to invest as you build your project.
Each project is divided into several achievable steps.
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.