Cluster Analysis : Unsupervised Machine Learning in Python


A Quick Way to Learn and Implement Clustering Algorithms for Pattern Recognition in Python. A Course for Beginners.

What you will learn

Describe the input and output of a clustering model

Prepare data with feature engineering techniques

Implement K-Means Clustering, Hierarchical Clustering, Mean Shift Clustering, DBSCAN, OPTICS and Spectral Clustering models

Determine the optimal number of clusters

Use a variety of performance metrics such as Silhouette Score, Calinski-Harabasz Index and Davies-Bouldin Index.

Description

Artificial intelligence and machine learning are touching our everyday lives in more-and-more ways. There’s an endless supply of industries and applications that machine learning can make more efficient and intelligent. You have probably come across Google News, which automatically groups similar news articles under a topic. Have you ever wondered what process runs in the background to arrive at these groups? Unsupervised machine learning is the underlying method behind a large part of this. Unsupervised machine learning algorithms analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without human intervention. This course introduces you to one of the prominent modelling families of Unsupervised Machine Learning called Clustering. This course provides the learners with the foundational knowledge to use Clustering models to create insights. You will become familiar with the most successful and widely used Clustering techniques, such as:

  • K-Means Clustering
  • Hierarchical Clustering
  • Mean Shift Clustering
  • DBSCAN : Density-Based Spatial Clustering of Applications with Noise
  • OPTICS : Ordering points to identify the clustering structure
  • Spectral Clustering

You will learn how to train clustering models to cluster and use performance metrics to compare different models. By the end of this course, you will be able to build machine learning models to make clusters using your data. The complete Python programs and datasets included in the class are also available for download. This course is designed most straightforwardly to utilize your time wisely. Get ready to do more learning than your machine!

Happy Learning.


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Career Growth:

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English
language

Content

Introduction

Introduction
Artificial Intelligence
Machine Learning
Supervised Learning
Supervised Learning: Classifications
Supervised Learning: Regressions
Unsupervised Learning
Unsupervised Learning : Clustering
Installation of Python Platform

Building and Evaluating Clustering ML Models

Important Terminologies
K-Means Clustering
Hierarchical Clustering
Silhouette Score
Calinski-Harabasz Index (Variance Ratio Criterion)
Davies-Bouldin Index
Mean Shift Clustering
DBSCAN : Density Based Spatial Clustering of Applications with Noise
OPTICS : Ordering points to identify the clustering structure
Spectral Clustering
Test your knowledge

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