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所在平台: Udemy |
课程主页: https://www.udemy.com/course/building-credit-card-fraud-detection-with-machine-learning/
课程评论：没有评论
课程名称：使用机器学习构建信用卡欺诈检测模型 课程概述：欢迎参加使用机器学习构建信用卡欺诈检测模型的课程。这是一门基于项目的综合课程，您将逐步学习如何使用逻辑回归、支持向量机和随机森林构建信用卡欺诈检测模型。本课程是机器学习与欺诈检测的完美结合，提供了提升数据科学技能的理想机会。 课程主要集中在三个方面：首先是数据分析，您将从多个角度探索信用卡数据集；其次是预测建模，您将学习如何使用大数据构建欺诈检测模型；最后是评估欺诈检测模型的准确性和性能。在介绍部分，您将学习欺诈检测模型的基本原理，包括常见挑战和实际应用。接着，我们将详细学习信用卡欺诈检测模型的完整流程，包括数据收集、特征提取、模型训练、实时处理和警报后的行动。 随后，您将学习最常见的信用卡欺诈案例，例如刷卡窃取、钓鱼攻击、身份盗窃、被盗卡、数据泄露和内部欺诈。一旦掌握了信用卡欺诈检测模型所需的所有知识，我们将开始项目。首先，您将逐步学习如何设置Google Colab IDE，并从Kaggle下载信用卡数据集。完成准备工作后，我们将进入项目的主要部分。 项目分为三个主要部分：第一部分是数据分析和可视化，您将从多个角度探索数据集；第二部分是学习如何使用逻辑回归、支持向量机和随机森林逐步构建信用卡欺诈检测模型；第三部分是学习如何评估模型的性能。课程最后，您将对欺诈检测模型进行测试，以确保其结果准确并正常运行。 课程的学习目标包括： - 理解欺诈检测模型的基本原理 - 学习信用卡欺诈检测模型的工作机制，包括数据收集、特征选择、模型训练、实时处理和后警报行动 - 了解最常见的信用卡欺诈案例 - 学习如何从Kaggle查找和下载数据集 - 学习清理数据集，包括删除缺失行和重复值 - 评估芯片和密码交易方法的安全性 - 分析和识别重复零售商欺诈模式 - 找到交易金额与欺诈之间的相关性 - 分析在线交易中的欺诈案例 - 使用随机森林进行特征选择 - 构建逻辑回归、随机森林和支持向量机的信用卡欺诈检测模型 - 使用准确率、召回率和F1分数评估欺诈检测模型的准确性和性能 本课程不仅有助于提升您的机器学习和数据科学技能，还有助于您在金融领域的职业发展。
Welcome to Building Credit Card Fraud Detection Model with Machine Learning course. This is a comprehensive project based course where you will learn step by step on how to build a credit card fraud detection model using logistic regression, support vector machine, and random forest. This course is a perfect combination between machine learning and fraud detection, making it an ideal opportunity to enhance your data science skills. The course will be mainly concentrating on three major aspects, the first one is data analysis where you will explore the credit card dataset from various angles, the second one is predictive modeling where you will learn how to build fraud detection model using big data, and the third one is to evaluate the fraud detection model's accuracy and performance. In the introduction session, you will learn the basic fundamentals of fraud detection models, such as getting to know its common challenges and practical applications. Then, in the next session, we are going to learn about the full step by step process on how the credit card fraud detection model works. This section will cover data collection, feature extraction, model training, real time processing, and post alert action. Afterwards, you will also learn about most common credit card fraud cases, for examples like card skimming, phishing attacks, identity theft, stolen card, data breaches, and insider fraud. Once you have learnt all necessary knowledge about the credit card fraud detection model, we will start the project. Firstly you will be guided step by step on how to set up Google Colab IDE. In addition to that, you will also learn how to find and download credit card dataset from Kaggle, Once, everything is ready, we will enter the main section of the course which is the project section The project will be consisted of three main parts, the first part is the data analysis and visualization where you will explore the dataset from multiple angles, in the second part, you will learn step by step on how to build credit card fraud detection model using logistic regression, support vector machine, and random forest, meanwhile, in the third part, you will learn how to evaluate the model's performance. Lastly, at the end of the course, you will conduct testing on the fraud detection model to make sure it produces accurate results and functions as it should.First of all, before getting into the course, we need to ask ourselves this question: why should we build a credit card fraud detection model? Well, here is my answer. In the past couple of years, we have witnessed a significant increase in the number of people conducting online transactions and, consequently, the risk of credit card fraud has surged. As technology advances, so do the techniques employed by fraudsters. Building a credit card fraud detection model becomes imperative to safeguard financial transactions, protect users from unauthorized activities, and maintain the integrity of online payment systems. By leveraging machine learning algorithms and data-driven insights, we can proactively identify and prevent fraudulent transactions. Last but not least, knowing how to build a complex fraud detection model can potentially open a lot of opportunities in the future.Below are things that you can expect to learn from this course:Learn the basic fundamentals of fraud detection modelLearn how credit card fraud detection models work. This section will cover data collection, feature selection, model training, real time processing, and post alert actionLearn about most common credit card fraud cases like stolen card, card skimming, phishing attack, identity theft, data breach, and insider fraudLearn how to find and download datasets from KaggleLearn how to clean dataset by removing missing rows and duplicate valuesLearn how to evaluate the security of chip and pin transaction methodsLearn how to analyze and identify repeat retailer fraud patternsLearn how to find correlation between transaction amount and fraudLearn how to analyze fraud cases in online transactionLearn how to conduct feature selection using Random ForestLearn how to build credit card fraud detection model using Random ForestLearn how to build credit card fraud detection model using Logistic RegressionLearn how to build credit card fraud detection model using Support Vector MachineLearn how to evaluate fraud detection model's accuracy and performance using precision, recall, and F1 score
