Fraud Detection in Python

Starting at USD 39.00
4 hours duration

Learn how to detect fraud using Python.

This course offers valuable knowledge and skills to combat fraud within organizations. It is estimated that a typical organization loses approximately 5% of its annual revenue to fraudulent activities. By leveraging data, you will learn effective strategies to detect and prevent fraud. Throughout the course, you will gain expertise in utilizing supervised learning algorithms to identify fraudulent behavior based on historical patterns. Additionally, you will explore unsupervised learning methods to uncover new types of fraudulent activities. One of the challenges in fraud analytics is dealing with highly imbalanced datasets, particularly when classifying fraud versus non-fraud cases. This course equips you with techniques to effectively address this issue. The curriculum encompasses a blend of technical and theoretical insights, enabling you to practically implement fraud detection models. You will receive hands-on guidance on implementing these models, ensuring a comprehensive understanding of the subject matter. Furthermore, the course offers invaluable tips and advice derived from real-life experiences, helping you avoid common mistakes in fraud analytics. By the end of this course, you will possess the necessary skills and knowledge to effectively combat fraud and safeguard your organization's financial well-being.

About Provider

DataCamp
DataCamp
Online Education Provider ยท 410 courses
Description
DataCamp is an online learning platform that offers interactive courses and tutorials for data science and analytics. It provides a wide range of courses covering topics such as Python, R, SQL, machine learning, data visualization, and more. The platform offers a hands-on learning experience through coding exercises and projects, allowing users to practice and apply their skills in real-world scenarios. DataCamp also offers a personalized learning experience with adaptive learning technology that adjusts the course content based on the user's skill level and progress. It is widely used by individuals, professionals, and organizations to enhance their data science skills and stay up-to-date with the latest trends and technologies in the field.
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