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Hands-On Machine Learning with Microsoft Excel 2019

You're reading from  Hands-On Machine Learning with Microsoft Excel 2019

Product type Book
Published in Apr 2019
Publisher Packt
ISBN-13 9781789345377
Pages 254 pages
Edition 1st Edition
Languages
Author (1):
Julio Cesar Rodriguez Martino Julio Cesar Rodriguez Martino
Profile icon Julio Cesar Rodriguez Martino
Toc

Table of Contents (17) Chapters close

Preface 1. Section 1: Machine Learning Basics
2. Implementing Machine Learning Algorithms 3. Hands-On Examples of Machine Learning Models 4. Section 2: Data Collection and Preparation
5. Importing Data into Excel from Different Data Sources 6. Data Cleansing and Preliminary Data Analysis 7. Correlations and the Importance of Variables 8. Section 3: Analytics and Machine Learning Models
9. Data Mining Models in Excel Hands-On Examples 10. Implementing Time Series 11. Section 4: Data Visualization and Advanced Machine Learning
12. Visualizing Data in Diagrams, Histograms, and Maps 13. Artificial Neural Networks 14. Azure and Excel - Machine Learning in the Cloud 15. The Future of Machine Learning 16. Assessment

Understanding unsupervised learning with clustering

Clustering is a statistical method that attempts to group the points in a dataset according to a distance measure, usually the Euclidean distance, which calculates the root of the squared differences between coordinates of a pair of points. To put this simply, those points that are classified within the same cluster are closer (in terms of the distance defined) to each other than they are to the points belonging to other clusters. At the same time, the larger the distance between two clusters, the better we can distinguish them. This is similar to saying that we try to build groups in which members are more alike and are more different to members of other groups.

It is clear that the most important part of a clustering algorithm is to define and calculate the distance between two given points and to iteratively assign the points...

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