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所在平台: Udemy |
课程主页: https://www.udemy.com/course/unsupervised-machine-learning/
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课程名称：无监督机器学习：包含两个顶点项目 课程概述： 如果你对无监督机器学习充满热情，这门课程将会非常适合你。该课程将一步一步带你进入无监督机器学习的世界。无监督机器学习利用机器学习算法分析和聚类未标记的数据集。这些算法能够发现隐藏的模式或数据分组，无需人工干预。它在信息相似性和差异性的发现方面表现出色，是探索性数据分析、交叉销售策略、客户细分和图像识别的理想解决方案。 本课程将为你提供无监督机器学习的理论知识和实践经验，内容生动有趣。我们将涵盖所有常见和重要的算法，并让你在一些真实项目中应用这些知识。课程内容包括： - K均值聚类 - 层次聚类 - DBSCAN 聚类 - 聚类分析的评估指标 - 处理维度的技术 - 各种聚类算法 - 处理不平衡数据的方法 - 相关性过滤 - 方差过滤 - 主成分分析（PCA）和线性判别分析（LDA） - 用于降维的t-SNE 我们详细覆盖了每一个主题，并学习如何将其应用于现实问题。课程资源终身可访问，并定期更新，以确保内容的时效性。
Crazy about Unsupervised Machine Learning?This course is a perfect fit for you.This course will take you step by step into the world of Unsupervised Machine Learning. Unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention. Its ability to discover similarities and differences in information make it the ideal solution for exploratory data analysis, cross-selling strategies, customer segmentation, and image recognition.This course will give you theoretical as well as practical knowledge of Unsupervised Machine Learning.This Unsupervised Machine Learning course is fun as well as exciting.It will cover all common and important algorithms and will give you the experience of working on some real-world projects.This course will cover the following topics:-K Means ClusteringHierarchical ClusteringDBSCAN ClusteringEvaluation Metrics for Clustering AnalysisTechniques used for Treating DimensionalityDifferent algorithms for clusteringDifferent methods to deal with imbalanced data.Correlation filteringVariance filteringPCA & LDAt-SNE for Dimensionality ReductionWe have covered each and every topic in detail and also learned to apply them to real-world problems.You will have lifetime access to the resources and we update the course regularly to ensure that its up to date.
