What you will 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.
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.
English
language
Content
Introduction to Clustering Analysis
Introduction to Clustering
Types of Clustering
Applications of Clustering
Using the Elbow Method for Choosing the Best Value for K
Introduction to K Means Clustering
Solving a Real World Problem
Implementing K Means on the Mall Dataset
Using Silhouette Score to analyze the clusters
Clustering Multiple Dimensions
Introduction to Hierarchical Clustering
Introduction to Dendrograms
Implementing Hierarchical Clustering
Introduction to DBSCAN Clustering
Implementing DBSCAN Clustering
Advanced Clustering Analysis
Introduction to BIRCH Clustering
Implementing BIRCH Clustering
Introduction to CURE Clustering
Implementing CURE Clustering
Introduction to Mini-Batch K-Means Clustering
Implementing Mini-Batch K-Means Clustering
Introduction to Mean Shift Clustering
Introduction to Mean Shift Clustering part 2
Implementing Mean Shift Clustering
Introduction to OPTICS Clustering
OPTICS Clustering V/S DBSCAN Clustering
Implementing OPTICS Clustering
Introduction to Spectral Clustering
Introduction to Spectral Clustering part 2
Implementing Spectral Clustering
Introduction to Gaussian Mixture Clustering
Gaussian Mixture Clustering V/S K-Means Clustering
Implementing Gaussian Mixture Clustering
Introduction to Kernel Density Estimation
Implementing Kernel Density Estimation
Optimizing Agricultural Production
Setting up the Environment
Understanding the Dataset
Understanding the Problem Statement
Performing Descriptive Statistics
Analyzing Agricultural Conditions
Clustering Similar Crops
Visualizing the Hidden Patterns
Predictive Modelling
Real Time Predictions
Summarizing the Key-Points
Quiz on Optimizing Agricultural Production
Customer Segmentation Engine
Understanding the Problem Statement
Setting up Environment
Data Analysis and Visualization
KMeans Clustering Analysis
Applying Hierarchical Clustering
Using Silhouette Score as Evaluation Metric
Three Dimensional Clustering
Major Learnings from the projects
Quiz on Customer Segmentation Engine
Outro Section
Conclusion
How to Get Your Certificate of Completion
Final Section
Bonus