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所在平台: Udemy |
课程主页: https://www.udemy.com/course/fraud-detection-using-python/
课程评论：没有评论
课程名称：Python中的欺诈检测 课程概述：如果您对利用机器学习检测欺诈感兴趣，那么本课程正适合您！欺诈是许多现代组织面临的重大问题，因为不法分子在方法论和技术能力上变得越来越复杂。因此，欺诈检测是一个重要的问题，永远无法完全解决。通过参加本课程，您将提升到一个可雇佣的技能水平，这在未来许多年中都将相关且重要。该课程由一位拥有机器学习博士学位的首席数据科学家开发，并在金融服务行业具有部署生产机器学习模型检测欺诈的实际经验。 在本课程中，学生将了解行业中欺诈问题，并学习如何通过引入各种机器学习方法加以解决。课程将通过一个具体的欺诈检测案例，提供建立模型的实践经验，使用Python进行操作。这包括应对欺诈问题时需要特别注意数据高度不平衡的挑战。课程内容包括： 第一课 - 欺诈检测简介：异常检测，类别不平衡 第二课 - 训练监督学习模型检测欺诈：逻辑回归，XGBoost，通过超参数优化提高性能 第三课 - 欺诈检测的性能指标：混淆矩阵，误分类成本，准确度悖论，在scikit-learn中实现指标 第四课 - 最优模型选择：基于性能指标的阈值优化，基于欺诈成本的阈值优化，引入Streamlit，构建阈值模拟器进行视觉检验 第五课 - 提升模型性能的策略：采样技术 每一课都建立在先前课程所取得的实践知识基础上，使学生能够完成一个端到端的项目，作为课程的最终输出。该项目可以成为学生项目组合中的重要部分，有助于他们的求职和职业发展。 在本课程中使用的Python技术栈包括：pandas、numpy、matplotlib、scikit-learn、seaborn、XGBoost、Streamlit和imblearn。
If you're interested in detecting fraud using machine learning, then this course is for you!Fraud is a massive problem for many modern organizations, as bad actors are becoming increasingly sophisticated both in methodology and technical ability. Detecting fraud is therefore an important problem that is never going to be completely solved. By taking this course, you'll be levelling up with a hireable skillset that is likely going to be relevant and for many years to come.This course was developed by myself, a Principal Data Scientist with a PhD in Machine Learning and real-world expertise in deploying production machine learning models for detecting fraud in the financial services industry.In this course, students will be introduced to the problem of fraud in industry, and how it can be solved via the introduction of various machine learning approaches. I will walk you through an example fraud detection problem, where you will get hands-on exposure to building models using Python. This will include navigating the challenging problem of fraud, where special consideration needs to be given to the highly imbalanced nature of the data. The lessons covered in this course include:Lesson 1 - Introduction to fraud detection: anomaly detection, class imbalanceLesson 2 - Training a supervised machine learning model to detect fraud: logistic regression, XGBoost, performance improvement through hyperparameter optimizationLesson 3 - Performance metrics for fraud detection: confusion matrix, cost of misclassification, accuracy paradox, implementing metrics in scikit-learnLesson 4 - Optimal model selection: threshold optimization using performance metrics, threshold optimization using cost of fraud, introduction to Streamlit, building a threshold simulator for visual inspectionLesson 5 - Strategies for improving model performance: sampling techniquesEach lesson builds on the practical knowledge achieved in the prior lessons, allowing for students to produce a completed end-to-end project as the final output of the course. This project could serve as an important part of a student's portfolio of projects, assisting with their job search and professional development endeavors.The Python technology stack used within this course includes the following: pandas, numpy, matplotlib, scikit-learn, seaborn, XGBoost, Streamlit and imblearn.
