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Hands-On Machine Learning with Microsoft Excel 2019

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

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781789345377
Length 254 pages
Edition 1st Edition
Tools
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Author (1):
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Julio Cesar Rodriguez Martino Julio Cesar Rodriguez Martino
Author Profile Icon Julio Cesar Rodriguez Martino
Julio Cesar Rodriguez Martino
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Table of Contents (17) Chapters Close

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

Summary

We have seen a step-by-step method to decompose a time series and forecast its future values. This can help us, at least in general terms, to predict the outcome of different processes. Time series can be studied both graphically and numerically, extracting their characteristics and using them to understand how they will behave in the future. We have also seen that this can be done automatically in Excel, with the risk of using it as a black box and not understanding the full forecasting method. More advanced techniques exist, and we will discuss them in future chapters.

The next chapter will show you how to build some basic diagrams in Excel and how to use them to gain insights on your datasets.

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