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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

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

Chapter 1, Implementing Machine Learning Algorithms

  1. In classical programming, the code developed and run in the computer is a step-by-step set of instructions telling the computer what to do and how to handle different options. Machine learning is about showing the computer examples of data to either teach it what to do by example, or to let it learn information that is hidden in the data.
  2. The machine learning models can be either regression (if the target variable is numerical and continuous) or classification (if the target variable is categorical or discrete).
  3. Models that learn by example, training on labeled data, are called supervised machine learning models. In comparison, those that find information in the unlabeled data are called unsupervised machine learning models.
  4. The following are the main steps that are needed when creating and using a machine learning model:
    1. Obtaining...
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