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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

Concepts

Machine Learning

Author (1):

Julio Cesar Rodriguez Martino

Table of Contents (17) Chapters

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

Summary

The last chapter of the book is thought of both as a summary of all chapters and as a window to what can be done beyond Excel and in the future. Automated data flows and machine learning model generation simplify analysts' work and speed up the decision-making process.

Hopefully, you now have a wide view of what machine learning is, how to use it in each line of business, and what are the most advanced alternatives to what was known before reading this book.

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Authors (1)

Julio Cesar Rodriguez Martino

Julio Cesar Rodriguez Martino is a machine learning (ML) and artificial intelligence (AI) platform architect, focusing on applying the latest techniques and models in these fields to optimize, automate, and improve the work of tax and accounting consultants. The main tool used in this practice is the MS Office platform, which Azure services complement perfectly by adding intelligence to the different tasks. Julio's background is in experimental physics, where he learned and applied advanced statistical and data analysis methods. He also teaches university courses and provides in-company training on machine learning and analytics, and has a lot of experience leading data science teams.

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