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You're reading from Hands-On Machine Learning with Microsoft Excel 2019 Build complete data analysis flows, from data collection to visualization

Product type Paperback

Published in Apr 2019

Publisher Packt

ISBN-13 9781789345377

Length 254 pages

Edition 1st Edition

Tools

Excel

Concepts

Data Analysis

Author (1):

Julio Cesar Rodriguez Martino

View More author details

Table of Contents (17) Chapters

Preface

1. Section 1: Machine Learning Basics FREE CHAPTER

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

Questions

Which characters can be used as separators in a text file?
Why is it so important to pre-process the data before loading it?
What is the difference between opening an Excel file and importing it?
What type of information can be imported from a web page?
JSON is one of the structured formats that's used to exchange information. What other formats exist?
Do some online research to understand the advantages of using databases over individual data files.

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Authors (1)

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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