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Deep Learning & Machine Learning

Techniques for detecting abnormal transactions using artificial intelligence

In the modern business world, data security and fraud prevention have become more important than ever. This lecture focuses on how to effectively detect and analyze fraudulent transactions using artificial intelligence (AI). Using Python and machine learning, we will build an AI model that can detect and detect anomalies in various industries, such as fraudulent transactions, credit card fraud, and production line abnormalities, early.

(4.6) 11 reviews

150 learners

  • YoungJea Oh
인공지능
이상치
주식
Machine Learning(ML)
Deep Learning(DL)

Reviews from Early Learners

What you will learn!

  • Method for detecting abnormal transactions

  • Fraud detection approach

  • Sampling Methods for Biased Data

  • LOF algorithm

  • Isolation Forest

  • Autoencoder principle

  • Variational Autoencoder

  • Mutational Autoencoder

  • Autoencoder

  • VAE

Brief theory, substantial practice.
Take on the challenge of artificial intelligence fraud detection!

AI-based outlier detection technique,
How far have you tried?

I recommend this to these people

🙋‍♀️ “I feel the limitations of traditional rule-based anomaly detection methods.”

🙋‍♀️ “I studied artificial intelligence, but where can I apply it?”

🙋‍♀️ “I need practical lectures that I can apply immediately in my work.”

This lecture covers outlier detection methods using artificial intelligence . Using AI models, we can detect unusual transactions and outliers early in various fields, including financial transactions, production, and manufacturing.

Implementing a fraud detection model requires a variety of machine learning techniques, including identifying fraudulent transaction patterns in data and sampling biased data.

So that you can understand both practice and principles at the same time

Traditional rule-based outlier detection and AI-based outlier detection techniques are completely different.

Therefore, this course's curriculum is designed to cover even the most recently developed machine learning techniques. By following the curriculum and practicing, you'll be able to apply it to building models for real-world outlier detection.

A to Z of new fraud detection methods!

  • ✅ Learn how to sample biased data.
  • ✅ Learn about supervised and unsupervised outlier detection techniques using traditional machine learning.
  • ✅ We will study supervised learning, unsupervised learning, and outlier detection techniques using deep learning.

Please check your player knowledge!

While this course is structured so that those with limited time can take it without any prerequisites, we recommend taking the following courses as a prerequisite. (Note: Basic knowledge of Python and ML/DL is required.)

If you want to quickly learn the basics of Python,

If you want to gradually acquire prior knowledge of machine learning/deep learning

If you want to learn the Python language properly and thoroughly

Recommended for
these people

Who is this course right for?

  • Anyone who wants to use artificial intelligence to detect outliers

  • Developers who feel the limitations of existing rule-based outlier detection

  • Information security professionals

Need to know before starting?

  • Python

  • Machine Learning, Deep Learning Basics

Hello
This is

3,770

Learners

295

Reviews

144

Answers

4.8

Rating

14

Courses

오랜 개발 경험을 가지고 있는 Senior Developer 입니다. 현대건설 전산실, 삼성 SDS, 전자상거래업체 엑스메트릭스, 씨티은행 전산부를 거치며 30 년 이상 IT 분야에서 쌓아온 지식과 경험을 나누고 싶습니다. 현재는 인공지능과 파이썬 관련 강의를 하고 있습니다.

홈페이지 주소:

https://ironmanciti.github.io/

Curriculum

All

37 lectures ∙ (11hr 8min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

All

11 reviews

4.6

11 reviews

  • shinmj87211486님의 프로필 이미지
    shinmj87211486

    Reviews 2

    Average Rating 5.0

    5

    89% enrolled

    I took this course because I had to learn the principles of anomaly detection using machine learning and deep learning in a short period of time, and I understood it well! However, as the instructor progressed, there were cases where he didn't code as if he was being chased by something and told me to do it myself and just skipped over it. I tend to enjoy coding one by one with the instructor while taking the class, so this part was a bit disappointing. However, I was very satisfied overall, so I think I'll take other classes!

    • degol5168님의 프로필 이미지
      degol5168

      Reviews 1

      Average Rating 5.0

      5

      32% enrolled

      • trimurti
        Instructor

        Thank you for the good review.

    • onkel1693님의 프로필 이미지
      onkel1693

      Reviews 2

      Average Rating 5.0

      5

      32% enrolled

      • trimurti
        Instructor

        Thank you for the good review.

    • uizukim2929님의 프로필 이미지
      uizukim2929

      Reviews 1

      Average Rating 5.0

      5

      100% enrolled

      • trimurti
        Instructor

        Thank you for the good review.

    • princekey님의 프로필 이미지
      princekey

      Reviews 14

      Average Rating 4.9

      5

      60% enrolled

      It was a helpful class.

      Limited time deal

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      17%

      $42.90

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