Guided Project - Credit Card Fraud Detection Using AI

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Guided Project - Credit Card Fraud Detection Using AI

This capstone project explores credit card fraud detection using artificial intelligence (AI) techniques. It provides hands-on experience with data analysis, machine learning, and fraud detection methodologies.

Self-Paced

Mentored

Intermediate

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Duration

2 weeks
4-6 Hours/Week
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Fee

$58

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This capstone project explores credit card fraud detection using artificial intelligence (AI) techniques. It provides hands-on experience with data analysis, machine learning, and fraud detection methodologies.

Participants will work through structured activities, including data preprocessing, exploratory data analysis (EDA), data visualization, and machine learning model development for fraud detection.

By the end of the project, learners will gain practical skills in building and evaluating fraud detection models, optimizing performance, and interpreting results effectively.

This course comprises 6 purposely designed modules that take you on a carefully defined learning journey.

It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules or submitting assignments. It is anticipated that it will take about seven hours to complete. However, as long as the course is completed by the end of your enrollment, you can work at your own pace. And dont worry, youre not alone! You will be encouraged to stay connected with your learning community and mentors through the course discussion space.

The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include reading material, hands-on labs, and online exams questions.

Once you have successfully completed the course, you will earn your Certificate of Completion.

Upon completing this project, learners will develop skills in:

  • Data Handling & Preprocessing
  • Exploratory Data Analysis (EDA)
  • Data Visualization
  • Machine Learning for Fraud Detection
  • Model Evaluation & Optimization

  • Data Analysts & Data Scientists
  • AI & Machine Learning Enthusiasts
  • Finance & Cybersecurity Professionals
  • Students & Academics

Learners should have prior knowledge in:

  • Python Programming
  • Data Wrangling & Preprocessing
  • Data Analysis & Visualization
  • Machine Learning Fundamentals