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所在平台: Udemy |
课程主页: https://www.udemy.com/course/outlier-detection-techniques/
课程评论：没有评论
**课程名称：** 数据挖掘与数据科学中的异常值检测算法 **课程概述：** 本课程全面教授异常值检测技术，旨在帮助数据科学家、分析师以及对金融欺诈检测（信用卡、保险、医疗）、网络安全入侵检测或军事侦察等领域感兴趣的学习者。课程不仅涵盖多种异常值检测技术的识别方法，更侧重于其实际的正确实施。无论您需要异常值检测应用于何种场景，本课程都将为您提供从理论到实践的知识，从基础算法循序渐进到更复杂的模型。此外，课程还支持Python、R和SAS等主流编程语言的算法实现，帮助您提升编程能力。本课程对统计学和线性代数没有先修知识的要求，非常适合初学者。 异常值检测（也称为离群点挖掘、离群点建模、新奇性检测或异常检测）在数据挖掘和机器学习领域有着广泛的应用，包括数据挖掘、机器学习、数据科学、模式识别、数据清洗、数据仓库、数据分析和统计学等。 本课程将介绍业界常用的热门算法，同时也会引入近几年数据挖掘领域新开发的高级方法。您将学习到在单变量空间、低维空间以及高维空间中用于检测异常值的算法。 教学方法强调对方法论细节的深入理解和对计算各阶段的掌握，而非单纯的编程练习。但对于热衷于编程的学习者，所有算法均已在R、Python和SAS中实现，方便下载和运行。 **学习内容列表：** * **单变量空间：** 1. 三西格玛法则（统计学，支持R + Python + SAS编程） 2. MAD（统计学，支持R + Python + SAS编程） 3. 箱线图法则（统计学，支持R + Python + SAS编程） 4. 调整箱线图法则（统计学，支持R + Python + SAS编程） * **低维空间：** 5. 马氏距离法则（统计学，支持R + Python + SAS编程） 6. LOF - 局部异常因子（数据挖掘，支持R + Python + SAS编程） * **高维空间：** 7. ABOD - 基于角度的异常值检测（数据挖掘，支持R + Python + SAS编程） 我们诚挚希望您能享受本次课程！ **课程目录：** 无
Welcome to the course " Outlier Detection Techniques ". Are you Data Scientist or Analyst or maybe you are interested in fraud detection for credit cards, insurance or health care, intrusion detection for cyber-security, or military surveillance for enemy activities? Welcome to Outlier Detection Techniques, a course designed to teach you not only how to recognise various techniques but also how to implement them correctly. No matter what you need outlier detection for, this course brings you both theoretical and practical knowledge, starting with basic and advancing to more complex algorithms. You can even hone your programming skills because all algorithms you'll learn have implementation in PYTHON, R and SAS. So what do you need to know before you get started? In short, not much! This course is perfect even for those with no knowledge of statistics and linear algebra. Why wait? Start learning today! Because Everyone, who deals with the data, needs to know "Outlier Detection Techniques"!The process of identifying outliers has many names in Data Mining and Machine learning such as outlier mining, outlier modeling, novelty detection or anomaly detection. Outlier detection algorithms are useful in areas such as: Data Mining, Machine Learning, Data Science, Pattern Recognition, Data Cleansing, Data Warehousing, Data Analysis, and Statistics.I will present you on the one hand, very popular algorithms used in industry, but on the other hand, i will introduce you also new and advanced methods developed in recent years, coming from Data Mining.You will learn algorithms for detection outliers in Univariate space, in Low-dimensional space and also learn innovative algorithm for detection outliers in High-dimensional space.I am convinced that only those who are familiar with the details of the methodology and know all the stages of the calculation, can understand it in depth. So, in my teaching method, I put a stronger emphasis on understanding the material, and less on programming. However, anyone who interested in programming, I developed all algorithms in R , Python and SAS, so you can download and run them. List of Algorithms:Univariate space:1. Three Sigma Rule ( Statistics , R + Python + SAS programming languages)2. MAD ( Statistics , R + Python + SAS programming languages )3. Boxplot Rule ( Statistics , R + Python + SAS programming languages )4. Adjusted Boxplot Rule ( Statistics , R + Python + SAS programming languages ) Low-dimensional Space:5. Mahalanobis Rule ( Statistics , R + Python + SAS programming languages )6. LOF - Local Outlier Factor ( Data Mining , R + Python + SAS programming languages)High-dimensional Space:7. ABOD - Angle-Based Outlier Detection ( Data Mining , R + Python + SAS programming languages) I sincerely hope you will enjoy the course.
