Cluster Analysis Unsupervised Machine Learning Course Bundle

Free Download Cluster Analysis Unsupervised Machine Learning Course Bundle
Published 1/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.17 GB | Duration: 6h 16m
Data science techniques for pattern recognition, data mining, k-means clustering, and hierarchical clustering etc.

What you’ll learn
How to use cluster analysis in data mining
About the various types of clusters
About the Marketing applications of cluster analysis
Implications of wide variety of clustering techniques
Use clustering in statistical analysis
Requirements
Basic knowledge of statistics is required. Some familiarity with data analysis will be considered as an added advantage though it is not a necessity.
Description
Cluster Analysis is a statistical tool which is used to classify objects into groups called clusters, where the objects belonging to one cluster are more similar to the other objects in that same cluster and the objects of other clusters are completely different. In simple words cluster analysis divides data into clusters that are meaningful and useful. Clustering is used mainly for two purposes – clustering for understanding and clustering for utility.Application of cluster analysisCluster analysis is used in many fields like machine learning, market research, pattern recognition, data analysis, information retrieval, image processing and data compression.Cluster analysis can help the marketers to find out distinct groups of their customer base.Cluster analysis is used in the field of biology to find out plant and animal taxonomies and categorize genes with similar characteristicsCluster analysis is used in an earth observation database to group the houses in a city according to the house type, value and location.Clustering can also be used to segment the documents on the web based on a specific criteriaIn data mining, cluster analysis is used to gain in-depth understanding about the characteristics of data in each cluster.Clustering MethodsClustering methods can be divided into the following categoriesPartitioning methodHierarchical MethodDensity based methodGrid Based MethodModel Based MethodConstraint Based MethodAdvantages of Cluster AnalysisGiven below are the advantages of cluster analysisCluster analysis gives a quick overview of dataIt can be used if there are many groups in dataCluster analysis can be used when there are unusual similarity measures to be doneCluster analysis can be added on ordination plots and it is good for the nearest neighboursApproaches to cluster analysisThere are a number of different approaches used to carry out cluster analysis which are divided into twoHierarchical Method – Agglomerative Methods and Divisive MethodsNon Hierarchical Method also known as K-means Clustering methodsCluster Analysis Course ObjectivesAt the end of this course you will be able to knowHow to use cluster analysis in data miningAbout the various types of clustersAbout the Marketing applications of cluster analysisImplications of wide variety of clustering techniquesUse clustering in statistical analysis
Overview
Section 1: Cluster Analysis and Unsupervised Machine Learning with MS Excel
Lecture 1 Introduction to Project
Lecture 2 Data Introduction
Lecture 3 Data Format and Selection
Lecture 4 Clustering Phase Part 1
Lecture 5 Clustering Phase Part 2
Lecture 6 Clustering Phase Part 3
Lecture 7 Clustering Phase Part 4
Lecture 8 Clustering Phase Part 5
Lecture 9 Clustering Phase Part 6
Lecture 10 Clustering Phase Part 7
Lecture 11 Clustering Phase Part 8
Lecture 12 Scatter Plot
Lecture 13 Cluster Analysis Final Phasing
Lecture 14 Scatter Plot
Lecture 15 Conclusion
Section 2: Cluster Analysis and Unsupervised Machine Learning
Lecture 16 Introduction of Project
Lecture 17 Import Libraries
Lecture 18 Data Preprocessing
Lecture 19 Pie chart
Lecture 20 Histogram
Lecture 21 Violin plot
Lecture 22 Distribution Plot Analysis
Lecture 23 Pair plot and Female Data Analysis
Lecture 24 Male Data Analysis
Lecture 25 Male Data Analysis Continue
Lecture 26 Correlation Analysis
Lecture 27 Modelling
Lecture 28 Cluster Prediction
Lecture 29 Shopping Analysis
Section 3: Cluster Analysis and Unsupervised Machine Learning
Lecture 30 Introduction to Project
Lecture 31 Clustering Overview
Lecture 32 Data Explanation
Lecture 33 Clustering Algorithm
Lecture 34 Clustering using scaled Variables
Section 4: Cluster Analysis and Unsupervised Machine Learning – Basic Concepts
Lecture 35 Meaning of Cluster Analysis
Lecture 36 Understanding Cluster Analysis through example
Lecture 37 Example on Cluster Analysis (continues)
Lecture 38 Hierarchical method of Clustering
Lecture 39 Single link clustering
Lecture 40 1-Linkage method,Wards method,k means clustering
Lecture 41 K means and Example of K means, difference between heirarchic
Lecture 42 Example of K means no. of cluster, Statistical tests, Dendogram, scree plot
Lecture 43 Two step cluster analysis.,Evaluation
Lecture 44 Example for Listwise and Pairwise deletion of missing values , SPSS windows of o
Lecture 45 K means cluster theory, spss windows for k means, listwise and pairwise deletion
Lecture 46 Two step cluster analysis
Students, Research professionals, Data Analysts, Data Miners And anyone who is interested in learning about cluster analysis

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