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Master Clustering Analysis using Python 2022
Become an expert and solve Real World Problems using Clustering Analysis and Python.
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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.
Advanced Clustering Analysis
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1
Introduction to Clustering -
2
Types of Clustering
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3
Applications of Clustering
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4
Using the Elbow Method for Choosing the Best Value for K
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5
Introduction to K Means Clustering
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6
Solving a Real World Problem
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7
Implementing K Means on the Mall Dataset
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8
Using Silhouette Score to analyze the clusters
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9
Clustering Multiple Dimensions
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10
Introduction to Hierarchical Clustering
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11
Introduction to Dendrograms
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12
Implementing Hierarchical Clustering
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13
Introduction to DBSCAN Clustering
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14
Implementing DBSCAN Clustering
Optimizing Agricultural Production
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15
Introduction to BIRCH Clustering
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16
Implementing BIRCH Clustering
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17
Introduction to CURE Clustering
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18
Implementing CURE Clustering
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19
Introduction to Mini-Batch K-Means Clustering
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20
Implementing Mini-Batch K-Means Clustering
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21
Introduction to Mean Shift Clustering
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22
Introduction to Mean Shift Clustering part 2
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23
Implementing Mean Shift Clustering
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24
Introduction to OPTICS Clustering
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25
OPTICS Clustering V/S DBSCAN Clustering
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26
Implementing OPTICS Clustering
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27
Introduction to Spectral Clustering
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28
Introduction to Spectral Clustering part 2
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29
Implementing Spectral Clustering
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30
Introduction to Gaussian Mixture Clustering
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31
Gaussian Mixture Clustering V/S K-Means Clustering
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32
Implementing Gaussian Mixture Clustering
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33
Introduction to Kernel Density Estimation
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34
Implementing Kernel Density Estimation
Customer Segmentation Engine
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35
Setting up the Environment
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36
Understanding the Dataset
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37
Understanding the Problem Statement
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38
Performing Descriptive Statistics
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39
Analyzing Agricultural Conditions
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40
Clustering Similar Crops
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41
Visualizing the Hidden Patterns
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42
Predictive Modelling
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43
Real Time Predictions
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44
Summarizing the Key-Points
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45
Quiz on Optimizing Agricultural Production
Outro Section
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46
Understanding the Problem Statement
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47
Setting up Environment
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48
Data Analysis and Visualization
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49
KMeans Clustering Analysis
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50
Applying Hierarchical Clustering
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51
Using Silhouette Score as Evaluation Metric
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52
Three Dimensional Clustering -
53
Major Learnings from the projects
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54
Quiz on Customer Segmentation Engine
Final Section
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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Course details
Video
3 hours
Lectures
3
Certificate of Completion
Full lifetime access
Access on mobile and TV
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