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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-mining-unsupervised-learning/
课程评论：没有评论
课程名称：数据挖掘 - 无监督学习 概述：数据挖掘 - 无监督学习课程旨在为学生提供无监督学习技术的全面理解。无监督学习是机器学习的一种类别，算法应用于未标记数据，以发现模式、结构和关系，而无需先前的知识或指导。整个课程中，学生将探索各种无监督学习算法及其在从大型数据集中挖掘隐藏洞察方面的应用。课程强调这些算法的基本原则、方法论和实际实施，而不是聚焦于数学推导。 课程开始于无监督学习的介绍，涵盖基本概念和目标。学生将学习无监督学习与监督学习和半监督学习的区别，以及无监督技术的优缺点。课程还将讨论数据预处理和数据准备的重要性，以确保结果的质量。 课程的第一个主要主题是聚类技术。学生将深入研究不同的聚类算法，如层次聚类、K均值聚类、基于密度的聚类（如DBSCAN）和期望最大化（EM）聚类。他们将学习如何应用这些算法将相似的数据点分组，并识别潜在的模式和结构。学生还将探讨在不同情景中选择适当聚类方法的挑战和考虑因素。 接下来，课程将转向降维，目的是在保留相关信息的同时减少数据集中的特征或变量数量。学生将探索主成分分析（PCA）、奇异值分解（SVD）和t分布随机邻域嵌入（t-SNE）等技术。他们将理解这些方法如何用于可视化高维数据并提取有助于分析和解释的有意义表示。 关联规则挖掘是课程的另一个关键主题。学生将学习流行的Apriori算法和FP-growth算法，这些算法用于发现事务数据集中项目之间有趣的关系和关联。他们将深入了解评估和解释关联规则的支持度、置信度和提升度量，以及它们在市场篮子分析和推荐系统中的实际应用。 课程还将讨论离群点检测，这是无监督学习中的一项重要任务。学生将探索统计方法，如z-score和修正z-score，以及基于距离的方法，如局部离群因子和孤立森林。他们将理解如何识别数据中的异常，这可以为欺诈检测、网络入侵检测或系统故障预测提供有价值的见解。 评估和验证无监督学习模型是课程的一个重要方面。学生将学习内部和外部评估指标，包括轮廓系数、纯度和Rand指数。他们将掌握评估聚类结果质量和测量降维技术性能的技能。 在整个课程中，学生将接触到无监督学习的各种实际应用。他们将发现如何通过聚类实现市场细分，使企业能够有效地针对特定客户群体。他们还将探索图像和文本聚类，这在图像识别、文件组织和推荐系统中具有应用。课程将强调异常检测在识别欺诈交易、网络入侵或制造缺陷中的关键角色。最后，学生将学习无监督学习如何驱动推荐系统，根据用户行为和偏好提供个性化推荐。 实践经验将成为课程的重要组成部分。学生将开展实际练习和项目，应用无监督学习算法于真实数据集，使用流行的数据挖掘工具和编程库，如Python的scikit-learn或R的caret包。他们将熟练掌握数据预处理、选择适当算法、调整参数以及解释和可视化结果。 通过课程的学习，学生将对无监督学习技术、其实用应用有扎实的理解，并具备利用这些方法从未标记数据中发现有价值的洞察和模式的能力。
The Data Mining - Unsupervised Learning course is designed to provide students with a comprehensive understanding of unsupervised learning techniques within the field of data mining. Unsupervised learning is a category of machine learning where algorithms are applied to unlabelled data to discover patterns, structures, and relationships without prior knowledge or guidance.Throughout the course, students will explore various unsupervised learning algorithms and their applications in uncovering hidden insights from large datasets. The emphasis will be on understanding the principles, methodologies, and practical implementation of these algorithms rather than focusing on mathematical derivations.The course will begin with an introduction to unsupervised learning, covering the basic concepts and goals. Students will learn how unsupervised learning differs from supervised learning and semi-supervised learning, and the advantages and limitations of unsupervised techniques. The importance of pre-processing and data preparation will also be discussed to ensure quality results.The first major topic of the course will be clustering techniques. Students will dive into different clustering algorithms such as hierarchical clustering, k-means clustering, density-based clustering (e.g., DBSCAN), and expectation-maximization (EM) clustering. They will learn how to apply these algorithms to group similar data points together and identify underlying patterns and structures. The challenges and considerations in selecting appropriate clustering methods for different scenarios will be explored.The course will then move on to dimensionality reduction, which aims to reduce the number of features or variables in a dataset while retaining relevant information. Students will explore techniques such as principal component analysis (PCA), singular value decomposition (SVD), and t-distributed stochastic neighbour embedding (t-SNE). They will understand how these methods can be used to visualize high-dimensional data and extract meaningful representations that facilitate analysis and interpretation.Association rule mining will be another key topic covered in the course. Students will learn about the popular Apriori algorithm and FP-growth algorithm, which are used to discover interesting relationships and associations among items in transactional datasets. They will gain insights into evaluating and interpreting association rules, including support, confidence, and lift measures, and their practical applications in market basket analysis and recommendation systems.The course will also address outlier detection, a critical task in unsupervised learning. Students will explore statistical approaches such as z-score and modified z-score, as well as distance-based approaches like the Local Outlier Factor and Isolation Forest. They will understand how to identify anomalies in data, which can provide valuable insights into potential fraud detection, network intrusion detection, or system failure prediction.Evaluation and validation of unsupervised learning models will be an essential aspect of the course. Students will learn about internal and external evaluation measures, including silhouette coefficient, purity, and Rand index. They will gain skills in assessing the quality of clustering results and measuring the performance of dimensionality reduction techniques.Throughout the course, students will be exposed to various real-world applications of unsupervised learning. They will discover how market segmentation can be achieved through clustering, enabling businesses to target specific customer segments effectively. They will also explore image and text clustering, which has applications in image recognition, document organization, and recommendation systems. The course will highlight anomaly detection, which plays a crucial role in identifying fraudulent transactions, network intrusions, or manufacturing defects. Lastly, students will learn how unsupervised learning powers recommender systems, providing personalized recommendations based on user behaviour and preferences.Hands-on experience will be a significant component of the course. Students will work on practical exercises and projects, applying unsupervised learning algorithms to real-world datasets using popular data mining tools and programming libraries such as Python's scikit-learn or R's caret package. They will gain proficiency in pre-processing data, selecting appropriate algorithms, fine-tuning parameters, and interpreting and visualizing the results.By the end of the course, students will have a solid understanding of unsupervised learning techniques, their practical applications, and the ability to leverage these methods to discover valuable insights and patterns from unlabelled data.
