About This Course

With the rise of e-commerce, online fraud has become a major concern for businesses. Fraud detection using machine learning has emerged as an effective solution for identifying fraudulent transactions in real-time. This course aims to provide students with the knowledge and skills required to build fraud detection models using machine learning techniques, with a focus on e-commerce fraud.

The course will cover the fundamental concepts of fraud detection. The course will be divided into 5 modules/sections, each covering a specific topic.

Introduction to Fraud Detection -> estimated time = 30 minutes

In the first module of this course, you will be introduced to what is fraud detection system and the purpose to create the fraud detection. Through a combination of text materials and engaging video tutorials, you will gain a comprehensive understanding of the underlying concepts and methodologies employed in fraud detection. Here are the sections that exist in this module:

Exploratory Data Analysis -> estimated time = 45 minutes

This module covers sections that delve into the intricacies of Exploratory Data Analysis. Through text materials, video tutorials, a theoretical quiz, and a coding assignment, you will gain a deep understanding of pre-processing techniques. To assess their understanding of the material covered in the video, you will be required to complete a quiz consisting of 10 multiple-choice questions. This assignment will provide you with hands-on experience in implementing these techniques, and will help to reinforce their understanding of the concepts covered in the module. Here are the sections that exist in this module:

Preprocessing Data -> estimated time = 45 minutes

This module covers sections that delve into the intricacies of data pre-processing. Through text materials, video tutorials, a theoretical quiz, and a coding assignment, you will gain a deep understanding of pre-processing techniques. To assess their understanding of the material covered in the video, you will be required to complete a quiz consisting of 10 multiple-choice questions. This assignment will provide you with hands-on experience in implementing these techniques, and will help to reinforce their understanding of the concepts covered in the module. Here are the sections that exist in this module:

Modelling and Evaluate Machine Learning -> estimated time = 15 minutes

This module covers sections that delve into the intricacies of building machine learning model and evaluate them. Through text materials, video tutorials, a theoretical quiz, and a coding assignment, you will gain a deep understanding of building machine learning model techniques. Learn how to build machine learning model for fraud detection system through a video-based learning approach. It also has text materials provided to make it easier for you to learn the module.

To assess their understanding of the material covered in the video, you will be required to complete a quiz consisting of 10 multiple-choice questions. This assignment will build upon the data preprocessing and feature selection techniques covered in the previous module, and will provide you with hands-on experience in applying machine learning techniques to real-world problems. Here are the sections that exist in this module:

By the end of this course, you will have a solid understanding of the fundamentals of fraud detection and how to build accurate fraud detection models using machine learning techniques, with a focus on e-commerce fraud. Students will also have the skills necessary to apply their models to real-world e-commerce transaction data and evaluate their performance.