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| File Name: | Clustering Analysis Masterclass using Python [2022 Edition] |
| Content Source: | https://www.udemy.com/course/master-clustering-analysis-using-python |
| Genre / Category: | Programming |
| File Size : | 2.8GB |
| Publisher: | udemy |
| Updated and Published: | February 23, 2022 |
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.
Enroll now and make the best use of this course.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.
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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