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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/ibm-unsupervised-machine-learning
课程评论：没有评论
课程名称：无监督机器学习 课程概述：本课程介绍了机器学习的主要类型之一：无监督学习。您将学习如何从没有目标或标签变量的数据集中挖掘见解。课程涵盖了多种无监督学习的聚类和降维算法，并教授如何选择最适合您数据的算法。课程的动手实践部分专注于使用无监督学习的最佳实践。 完成本课程后，您将能够： - 解释适合无监督学习方法的问题类型 - 解释维度诅咒及其对多特征聚类的影响 - 描述并使用常见的聚类和降维算法 - 尝试在适当的情况下对点进行聚类，比较每个聚类模型的性能 - 理解与聚类相关的指标 适合人群：本课程面向渴望在商业环境中掌握无监督机器学习技术的有志数据科学家。 所需技能：为充分利用本课程，您需要熟悉Python开发环境编程，以及具备数据清洗、探索性数据分析、微积分、线性代数、概率和统计的基本知识。 课程大纲： 1. 部分一：无监督学习与K均值简介 - 描述：本模块介绍无监督学习及其应用，特别是使用K均值聚类观察。在此模块中，您将熟悉该算法背后的理论，并通过演示进行实践。 2. 部分二：选择聚类算法 - 描述：本模块将使您了解聚类算法中的一些计算难点，以及不同聚类实现如何克服这些难点。在回顾常见聚类算法之后，您将学习如何比较它们并选择最适合您数据的聚类技术。 3. 部分三：降维 - 描述：本模块介绍降维和主成分分析(PCA)，这些是用于大数据、影像和数据预处理的强大技术。在本模块结束时，您将拥有展示无监督学习能力所需的所有工具，以便在最终项目中应用。
Part: 1
Title:Introduction to Unsupervised Learning and K Means
Description:This module introduces Unsupervised Learning and its applications. One of the most common uses of Unsupervised Learning is clustering observations using k-means. In this module you become familiar with the theory behind this algorithm, and put it in practice in a demonstration.
Part: 2
Title:Selecting a clustering algorithm
Description:In this module you become familiar with some of the computational hurdles around clustering algorithms, and how different clustering implementations try to overcome them. After a brief recapitulation of common clustering algorithms, you will learn how to compare them and select the clustering technique that best suits your data.
Part: 3
Title:Dimensionality Reduction
Description:This module introduces dimensionality reduction and Principal Component Analysis, which are powerful techniques for big data, imaging, and pre-processing data. At the end of this module, you will have all the tools in your toolkit to highlight your Unsupervised Learning abilities in your final project.
This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning. By the end of this course you should be able to: Explain the kinds of problems suitable for Unsupervised Learning approaches Explain the curse of dimensionality, and how it makes clustering difficult with many features Describe and use common clustering and dimensionality-reduction algorithms Try clustering points where appropriate, compare the performance of per-cluster models Understand metrics relevant for characterizing clusters Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting. What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.
