基于Python 2022的主聚类分析 Master Clustering Analysis using Python 2022

成为专家，使用聚类分析和Python解决现实世界的问题

你会学到什么
获得聚类分析的介绍。
了解聚类分析的类型和应用。
了解多维聚类。
获得K均值算法的介绍。
介绍并实现了K均值聚类。
获得肘部方法的介绍。
获得剪影方法的介绍。
实现K均值聚类。
获得层次聚类的介绍。
实现分层聚类。
获得介绍并实现数据库扫描集群。
获得BIRCH集群的介绍和实现。
获取CURE集群的介绍和实现。
获得小批量K-均值聚类的介绍和实现。
得到均值漂移聚类的介绍和实现。
获得光学群集的介绍和实现。
了解光学群集垂直/垂直数据库扫描群集。
获得谱聚类的介绍和实现。
得到高斯混合聚类的介绍和实现。
了解高斯混合聚类。
介绍并实现核密度估计。


MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz，2 Ch
语言：英语+中英文字幕（云桥CG资源站 机译） |时长:55节课(3h 10m) |大小解压后:2.98 GB

要求
计算机和互联网的可用性。
Python必须安装在您的计算机上。
需要Python编程语言的基础知识。

描述
欢迎来到精彩的聚类分析在线课程。

聚类分析是数据分析工具包中的众多工具之一，可用于分析数据和寻找关联模式。聚类分析试图通过对属性进行分组来确定一组对象或事件的结构或层次。

本课程最适合您使用Python掌握聚类分析。它涵盖了从基本到高级的聚类分析概念。


在本课程中，您将包括

聚类分析导论。

了解集群的类型和应用。

K均值聚类的介绍与实现。

肘部和轮廓方法的实现。

了解多维聚类。

了解树木图。

层次聚类的介绍与实现。

了解数据库扫描集群及其实现。

了解BIRCH集群及其实现。

了解CURE集群及其实现。

了解小批量K均值聚类及其实现。

了解均值漂移聚类及其实现。

了解光学集群及其实现。

还要学习光学聚类。

了解光谱聚类及其实现。

了解高斯混合聚类及其实现。

还要学习高斯混合聚类。

了解核密度估计及其实现。

完成本课程后，您将成为聚类分析专家。我们还提供测验。

您还可以访问本课程中使用的所有资源。

讲师支持-针对任何疑问的快速讲师支持。

这门课是给谁的
对机器学习和数据科学感兴趣的学生和专业人士。
想要介绍无监督机器学习和聚类分析的人。
想知道如何编写自己的集群代码的人。
任何数据科学家。
研究人员、企业家、讲师等。
任何想分析数据的人。


MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 55 lectures (3h 10m) | Size: 2.85 GB

Become an expert and solve Real World Problems using Clustering Analysis and Python

What you’ll learn
Get an Introduction to Clustering Analysis.
Understand the Types and Applications of Clustering Analysis.
Learn about the Clustering Multiple Dimensions.
Get an Introduction to K Means Algorithm.
Introduction and Implement the K Means Clustering.
Get an Introduction to Elbow Method.
Get an Introduction to Silhouette Method.
Implement the K Means Clustering.
Get an Introduction to Hierarchical Clustering.
Implement Hierarchical Clustering.
Get an Introduction and Implement DBSCAN Clustering.
Get introduction and implementation of BIRCH Clustering.
Get introduction and implementation of CURE Clustering.
Get introduction and implementation of Mini-Batch K-Means Clustering.
Get introduction and implementation of Mean Shift Clustering.
Get introduction and implementation of OPTICS Clustering.
Learn about the OPTICS Clustering V/S DBSCAN Clustering.
Get introduction and implementation of Spectral Clustering.
Get introduction and implementation of Gaussian Mixture Clustering.
Learn about Gaussian Mixture Clustering V/S K-Means Clustering.
Get introduction and implementation of Kernel Density Estimation.

Requirements
Availability computer and internet.
Python must be installed on your computer.
Basic knowledge of Python programming language is required.

Description
Welcome to the wonderful online course of Clustering Analysis.

Clustering analysis is one of many tools in the data analytics toolkit which can be used to analyze data and find patterns of association. Clustering analysis attempts to determine the structure or hierarchy of a set of objects or events through grouping attributes.

This course is best for you to master Clustering Analysis using Python. It covers basic to advanced level of Clustering Analysis concepts.

In this course, you will cover:-

Introduction to Clustering Analysis.

Learn about the Types and Applications of Clustering.

Introduction and Implementation of K Means Clustering.

Implementation of Elbow and Silhouette method.

Learn about the Clustering Multiple Dimensions.

Learn about the Dendrograms.

Introduction and Implementation of Hierarchical Clustering.

Learn about the DBSCAN Clustering and its implementation.

Learn about the BIRCH Clustering and its implementation.

Learn about the CURE Clustering and its implementation.

Learn about the Mini-Batch K-Means Clustering and its implementation.

Learn about the Mean Shift Clustering and its implementation.

Learn about the OPTICS Clustering and its implementation.

Also learn OPTICS Clustering V/S DBSCAN Clustering.

Learn about the Spectral Clustering and its implementation.

Learn about the Gaussian Mixture Clustering and its implementation.

Also learn Gaussian Mixture Clustering V/S K-Means Clustering.

Learn about the Kernel Density Estimation and its implementation.

After finishing this course, you will become an expert in Clustering Analysis. We are also providing quizzes.

You will also have access to all the resources used in this course.

Instructor Support – Quick Instructor Support for any queries.

Who this course is for
Students and professionals interested in machine learning and data science.
People who want an introduction to unsupervised machine learning and cluster analysis.
People who want to know how to write their own clustering code.
Anyone who is a Data Scientists.
Researchers, Entrepreneurs, Instructors, etc.
Anyone who want to analyze the data.
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