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所在平台: Udemy |
课程主页: https://www.udemy.com/course/kmeans-clustering/
课程评论：没有评论
课程名称：K-Means聚类分析与无监督学习 内容概述： 本课程旨在深入介绍K-Means算法，作为理解无监督学习和聚类分析的有力工具。聚类是机器学习，尤其是日益重要的无监督学习领域中的关键技术。 课程亮点： * **直观理解算法原理：** 首先，课程将通过可视化方式，在不引入复杂数学公式的情况下，帮助学员建立对K-Means算法核心机制和数据聚类行为的直观认识。随后，将详细阐释该算法的数学背景。 * **Python实现：** 学员将学习如何从零开始，仅使用NumPy库来实现K-Means算法，从而深入掌握其工作原理。此外，还将介绍如何使用一行代码快速实现K-Means。课程示例将基于自行生成的人工数据。 * **应用注意事项：** 课程将重点讲解K-Means算法的优势及其局限性。学员将学习何时适合使用该算法，何时应避免使用，并通过具体的正反面案例来理解其质量和适用性。
Learn why and where K-Means is a powerful toolClustering is a very important part of machine learning. Especially unsupervised machine learning is a rising topic in the whole field of artificial intelligence. If we want to learn about cluster analysis, there is no better method to start with, than the k-means algorithm.Get a good intuition of the algorithmThe K-Means algorithm is explained in detail. We will first cover the principle mechanics without any mathematical formulas, just by visually observing data points and clustering behavior. After that, the mathematical background of the method is explained in detail.Learn how to implement the algorithm in PythonFirst we will learn how to implement K-Means from scratch. That means for the beginning no additional packages will be used, except numpy. This is important to get a really good grip on the functioning of the algorithm.You will of course also learn how to implement the algorithm really quickly by using only one line of code.The examples will be based on artificial data, which we generate ourselves in the course.Learn where you should pay attentionK-Means is a powerful tool but it definetely has drawbacks! You will learn where you have to be careful and when you should use the algorithm, and also when it is a bad idea to use the algorithm. I will show you examples and counterexamples on the quality and applicability of this method.
