Anomaly Detection and Fraud Analytics Virtual Internship
In this virtual internship, students will learn how to build machine learning models to detect anomalies and identify fraudulent activities in financial transactions and other sensitive data. They will gain hands-on experience in data preprocessing, feature engineering, model selection, and performance evaluation. By the end of the internship, students will be able to apply their skills to real-world fraud detection problems and contribute to the development of robust fraud analytics systems.
Track Overview
Tasks & Milestones
Understanding Fraud Patterns in Financial Data
IntermediateIn this task, students will explore a dataset of financial transactions and identify common fraud patterns.
Handling Missing Values and Outliers
IntermediateIn this task, students will learn how to preprocess a financial dataset by addressing missing values and outliers.
Feature Engineering for Fraud Detection
IntermediateIn this task, students will learn how to create meaningful features from financial data to improve the performance of fraud detection models.
Supervised Learning for Fraud Detection
IntermediateIn this task, students will build and evaluate supervised machine learning models for fraud detection.
Unsupervised Learning for Anomaly Detection
IntermediateIn this task, students will explore unsupervised learning techniques for detecting anomalies in financial data.
Deploying Fraud Detection Models
IntermediateIn this task, students will learn how to package and deploy their fraud detection models in a production environment.
Maintaining Fraud Detection Models
IntermediateIn this task, students will learn strategies for maintaining the performance of fraud detection models over time.
Prerequisites
- • Proficiency in Python programming
- • Basic understanding of machine learning concepts
- • Experience with data manipulation using libraries like Pandas
Certificate
Certificate of Completion
Earn a certificate upon successful completion