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Ahmad Varasteh
In this 2-hour guided project, you will learn how to leverage Generative AI for data generation to address data imbalance. SecureTrust Financial Services, a financial institution, has asked us to help them improve the accuracy of their fraud detection system. The model is a binary classifier, but it's not performing well due to data imbalance. As data scientists, we will employ Generative Adversarial Networks (GANs), a subset of Generative AI, to create synthetic fraudulent transactions that closely resemble real transactions. This approach aims to balance the dataset and enhance the accuracy of the fraud detection model.
Read more
In this 2-hour guided project, you will learn how to leverage Generative AI for data generation to address data imbalance. SecureTrust Financial Services, a financial institution, has asked us to help them improve the accuracy of their fraud detection system. The model is a binary classifier, but it's not performing well due to data imbalance. As data scientists, we will employ Generative Adversarial Networks (GANs), a subset of Generative AI, to create synthetic fraudulent transactions that closely resemble real transactions. This approach aims to balance the dataset and enhance the accuracy of the fraud detection model.
Read more
In this 2-hour guided project, you will learn how to leverage Generative AI for data generation to address data imbalance. SecureTrust Financial Services, a financial institution, has asked us to help them improve the accuracy of their fraud detection system. The model is a binary classifier, but it's not performing well due to data imbalance. As data scientists, we will employ Generative Adversarial Networks (GANs), a subset of Generative AI, to create synthetic fraudulent transactions that closely resemble real transactions. This approach aims to balance the dataset and enhance the accuracy of the fraud detection model.
This guided project is designed for those interested in learning how Generative models can increase model accuracy by generating synthetic data. To make the most of this project, it is recommended to have at least one year of experience using deep learning frameworks such as TensorFlow and Keras in Python.
Register for this course and see more details by visiting:
OpenCourser.com/course/1dbbn2/data
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What's inside
Syllabus
Traffic lights
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what should give you pause and
possible dealbreakers
Course explores relevant use case of AI in financial services
Taught by recognized experts in Generative AI
Develops practical skills in synthetic data generation
Prerequisites may make course less accessible to beginners
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Reviews summary
Practical gans for imbalanced data
According to students, this course provides a highly practical and hands-on experience in leveraging Generative AI, specifically GANs, to tackle the critical issue of data imbalance in fields like credit card fraud detection. Learners found the project format to be focused and effective for applying concepts. While it offers a valuable demonstration of synthetic data generation, prospective students should note that it assumes a strong background with deep learning frameworks like TensorFlow and Keras. It is best suited for those looking for a direct application rather than a deep theoretical dive.
Provides a structured, step-by-step application rather than theoretical depth.
"The guided project format is perfect for quick practical learning, though not a deep dive into GAN theory."
"If you're looking for a theoretical understanding of GANs, this is more of a practical demo."
"I appreciate the clear steps and runnable code, making it easy to follow along."
Requires solid deep learning and Python experience to follow effectively.
"Definitely recommend having strong TensorFlow/Keras knowledge beforehand."
"As stated, it's not for beginners; the pace assumes prior deep learning exposure."
"I struggled with some concepts without a year of experience in deep learning."
Effectively demonstrates using Gen AI to solve imbalanced dataset challenges.
"The core concept of balancing data with synthetic generation was very well demonstrated."
"This course offers a novel approach to a common problem in machine learning."
"Understanding how to improve classifier accuracy with GANs for imbalanced data was key for me."
Offers valuable hands-on experience solving real-world data issues.
"This project gave me exactly what I needed to apply GANs to imbalanced datasets."
"I found the credit card fraud detection scenario very relevant and practical."
"It’s great to see how Generative AI can directly improve model accuracy."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Data Balancing with Gen AI: Credit Card Fraud Detection with these
activities:
Review basic Statistics
Show steps
Review the fundamentals of Statistics to strengthen your foundation for this course.
Browse courses on
Statistics
Show steps
-
Go over your notes from a previous Statistics course.
-
Take a practice quiz or exam to test your understanding.
-
Review online tutorials or videos on basic statistical concepts.
Attend a local Meetup or conference on AI
Show steps
Expand your network and learn from others in the field by attending a local AI Meetup or conference.
Browse courses on
AI
Show steps
-
Find a local Meetup or conference that aligns with your interests.
-
Attend the event and actively participate in discussions.
-
Connect with other attendees and exchange knowledge.
Follow tutorials on GANs
Show steps
Deepen your understanding of GANs by following guided tutorials and working through examples.
Browse courses on
Generative Adversarial Networks
Show steps
-
Find reputable online tutorials or courses on GANs.
-
Follow the tutorials step-by-step, implementing the code and experimenting with different parameters.
-
Review the documentation and research papers related to GANs to enhance your understanding.
Two other activities
Expand to see all activities and additional details
Show all five activities
Solve coding challenges on GANs
Show steps
Sharpen your coding skills and reinforce your understanding of GANs by solving coding challenges.
Browse courses on
GANs
Show steps
-
Find online coding challenges or platforms that focus on GANs.
-
Solve the challenges, implementing solutions in your preferred programming language.
-
Review your solutions and identify areas for improvement.
Build a GAN model from scratch
Show steps
Solidify your knowledge by building a GAN model from the ground up, customizing it to address the specific data imbalance challenge.
Browse courses on
GANs
Show steps
-
Design the architecture of your GAN model, including the generator and discriminator networks.
-
Implement the model in your preferred deep learning framework, such as TensorFlow or PyTorch.
-
Train and evaluate your model on the provided imbalanced dataset.
Review basic Statistics
Show steps
Review the fundamentals of Statistics to strengthen your foundation for this course.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Go over your notes from a previous Statistics course.
- Take a practice quiz or exam to test your understanding.
- Review online tutorials or videos on basic statistical concepts.
Attend a local Meetup or conference on AI
Show steps
Expand your network and learn from others in the field by attending a local AI Meetup or conference.
Browse courses on
AI
Show steps
Show steps
Show steps
- Find a local Meetup or conference that aligns with your interests.
- Attend the event and actively participate in discussions.
- Connect with other attendees and exchange knowledge.
Follow tutorials on GANs
Show steps
Deepen your understanding of GANs by following guided tutorials and working through examples.
Browse courses on
Generative Adversarial Networks
Show steps
Show steps
Show steps
- Find reputable online tutorials or courses on GANs.
- Follow the tutorials step-by-step, implementing the code and experimenting with different parameters.
- Review the documentation and research papers related to GANs to enhance your understanding.
Two other activities
Expand to see all activities and additional details
Show all five activities
Solve coding challenges on GANs
Show steps
Sharpen your coding skills and reinforce your understanding of GANs by solving coding challenges.
Browse courses on
GANs
Show steps
Show steps
Show steps
- Find online coding challenges or platforms that focus on GANs.
- Solve the challenges, implementing solutions in your preferred programming language.
- Review your solutions and identify areas for improvement.
Build a GAN model from scratch
Show steps
Solidify your knowledge by building a GAN model from the ground up, customizing it to address the specific data imbalance challenge.
Browse courses on
GANs
Show steps
Show steps
Show steps
- Design the architecture of your GAN model, including the generator and discriminator networks.
- Implement the model in your preferred deep learning framework, such as TensorFlow or PyTorch.
- Train and evaluate your model on the provided imbalanced dataset.
Career center
Learners who complete Data Balancing with Gen AI: Credit Card Fraud Detection will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists gather and analyze data to help businesses make better decisions. Generative models are a powerful tool for data scientists, and this course provides a practical introduction to using GANs to solve real-world problems like data imbalance. By taking this course, you'll learn how to use GANs to generate synthetic data, which can be used to improve the accuracy of machine learning models. This course is especially relevant for Data Scientists who are working on projects involving imbalanced data.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models. This course provides a practical introduction to using Generative Adversarial Networks (GANs), a powerful tool for generating synthetic data. GANs can be used to solve a variety of problems in machine learning, including data imbalance. By taking this course, you'll learn how to use GANs to improve the accuracy of your machine learning models. This course is especially relevant for Machine Learning Engineers who are working on projects involving imbalanced data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make investment decisions. This course provides a practical introduction to using Generative Adversarial Networks (GANs), a powerful tool for generating synthetic data. GANs can be used to solve a variety of problems in finance, including data imbalance. By taking this course, you'll learn how to use GANs to improve the accuracy of your financial models. This course is especially relevant for Quantitative Analysts who are working on projects involving imbalanced data.
Risk Analyst
Risk Analyst
Risk Analysts identify and assess risks to businesses. This course provides a practical introduction to using Generative Adversarial Networks (GANs), a powerful tool for generating synthetic data. GANs can be used to solve a variety of problems in risk management, including data imbalance. By taking this course, you'll learn how to use GANs to improve the accuracy of your risk models. This course is especially relevant for Risk Analysts who are working on projects involving imbalanced data.
Fraud Analyst
Fraud Analyst
Fraud Analysts investigate and prevent fraud. This course provides a practical introduction to using Generative Adversarial Networks (GANs), a powerful tool for generating synthetic data. GANs can be used to solve a variety of problems in fraud detection, including data imbalance. By taking this course, you'll learn how to use GANs to improve the accuracy of your fraud detection models. This course is especially relevant for Fraud Analysts who are working on projects involving imbalanced data.
Data Engineer
Data Engineer
Data Engineers design and build the infrastructure that stores and processes data. This course provides a practical introduction to using Generative Adversarial Networks (GANs), a powerful tool for generating synthetic data. GANs can be used to solve a variety of problems in data engineering, including data imbalance. By taking this course, you'll learn how to use GANs to improve the efficiency and accuracy of your data pipelines. This course is especially relevant for Data Engineers who are working on projects involving imbalanced data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course provides a practical introduction to using Generative Adversarial Networks (GANs), a powerful tool for generating synthetic data. GANs can be used to solve a variety of problems in software engineering, including data imbalance. By taking this course, you'll learn how to use GANs to improve the quality and efficiency of your software development process. This course is especially relevant for Software Engineers who are working on projects involving imbalanced data.
Business Analyst
Business Analyst
Business Analysts analyze business processes and systems to identify areas for improvement. This course provides a practical introduction to using Generative Adversarial Networks (GANs), a powerful tool for generating synthetic data. GANs can be used to solve a variety of problems in business analysis, including data imbalance. By taking this course, you'll learn how to use GANs to improve the accuracy and efficiency of your business analysis.
Product Manager
Product Manager
Product Managers develop and manage products. This course provides a practical introduction to using Generative Adversarial Networks (GANs), a powerful tool for generating synthetic data. GANs can be used to solve a variety of problems in product management, including data imbalance. By taking this course, you'll learn how to use GANs to improve the quality and efficiency of your product development process. This course is especially relevant for Product Managers who are working on products that involve imbalanced data.
Consultant
Consultant
Consultants provide advice and guidance to businesses and organizations. This course provides a practical introduction to using Generative Adversarial Networks (GANs), a powerful tool for generating synthetic data. GANs can be used to solve a variety of problems in consulting, including data imbalance. By taking this course, you'll learn how to use GANs to improve the quality and efficiency of your consulting services. This course is especially relevant for Consultants who are working on projects involving imbalanced data.
Researcher
Researcher
Researchers conduct research to advance knowledge and understanding. This course provides a practical introduction to using Generative Adversarial Networks (GANs), a powerful tool for generating synthetic data. GANs can be used to solve a variety of problems in research, including data imbalance. By taking this course, you'll learn how to use GANs to improve the quality and efficiency of your research. This course is especially relevant for Researchers who are working on projects involving imbalanced data.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. This course provides a practical introduction to using Generative Adversarial Networks (GANs), a powerful tool for generating synthetic data. GANs can be used to solve a variety of problems in statistics, including data imbalance. By taking this course, you'll learn how to use GANs to improve the quality and efficiency of your statistical analysis. This course is especially relevant for Statisticians who are working on projects involving imbalanced data.
Data Analyst
Data Analyst
Data Analysts analyze data to identify trends and patterns. This course provides a practical introduction to using Generative Adversarial Networks (GANs), a powerful tool for generating synthetic data. GANs can be used to solve a variety of problems in data analysis, including data imbalance. By taking this course, you'll learn how to use GANs to improve the quality and efficiency of your data analysis. This course is especially relevant for Data Analysts who are working on projects involving imbalanced data.
Financial Analyst
Financial Analyst
Financial Analysts provide guidance to individuals and organizations on financial matters. This course provides a practical introduction to using Generative Adversarial Networks (GANs), a powerful tool for generating synthetic data. GANs can be used to solve a variety of problems in financial analysis, including data imbalance. By taking this course, you'll learn how to use GANs to improve the quality and efficiency of your financial analysis. This course is especially relevant for Financial Analysts who are working on projects involving imbalanced data.
Marketing Analyst
Marketing Analyst
Marketing Analysts analyze marketing data to identify trends and patterns. This course provides a practical introduction to using Generative Adversarial Networks (GANs), a powerful tool for generating synthetic data. GANs can be used to solve a variety of problems in marketing analysis, including data imbalance. By taking this course, you'll learn how to use GANs to improve the quality and efficiency of your marketing analysis. This course is especially relevant for Marketing Analysts who are working on projects involving imbalanced data.
For more career information including salaries, visit:
OpenCourser.com/course/1dbbn2/data
Reading list
We've selected eight books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Data Balancing with Gen AI: Credit Card Fraud Detection.
Introduces advanced deep learning techniques, including GANs. Provides hands-on examples and exercises to help learners apply these techniques in practice.
Provides a comprehensive overview of deep learning, covering the fundamental concepts, architectures, and applications. It highly recommended resource for anyone interested in learning about deep learning.
Explores the creative applications of generative deep learning, including GANs. Provides insights into how GANs can be used to generate art, music, and text.
Teaches deep learning fundamentals and practical implementation using Fastai and PyTorch. Covers GANs and provides code examples for implementing them.
Provides a practical guide to data science, covering the entire data science process from data collection to model deployment. It valuable resource for data scientists and business professionals.
Provides a comprehensive overview of statistical learning, covering the fundamental concepts, algorithms, and applications. It highly recommended resource for anyone interested in learning about statistical learning.
Introduces AI and machine learning concepts to coders. Covers basic techniques and algorithms, including data preprocessing, model training, and evaluation.
Save
Provides a hands-on introduction to GANs. Covers the basics of GANs, their architectures, and their applications in various domains.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1dbbn2/data
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