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Hands-On Machine Learning with Microsoft Excel 2019

 

Hands-On Machine Learning with Microsoft Excel 2019

Autor: Julio Cesar Rodriguez Martino
Broj strana: 254
ISBN broj: 9781789345377
Izdavač: PACKT PUBLISHING
Godina izdanja: 2019.

Pregleda (30 dana / ukupno): 88 / 88

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  • Use Excel to preview and cleanse datasets
  • Understand correlations between variables and optimize the input to machine learning models
  • Use and evaluate different machine learning models from Excel
  • Understand the use of different visualizations
  • Learn the basic concepts and calculations to understand how artificial neural networks work
  • Learn how to connect Excel to the Microsoft Azure cloud
  • Get beyond proof of concepts and build fully functional data analysis flows

We have made huge progress in teaching computers to perform difficult tasks, especially those that are repetitive and time-consuming for humans. Excel users, of all levels, can feel left behind by this innovation wave. The truth is that a large amount of the work needed to develop and use a machine learning model can be done in Excel. The book starts by giving a general introduction to machine learning, making every concept clear and understandable. Then, it shows every step of a machine learning project, from data collection, reading from different data sources, developing models, and visualizing the results using Excel features and offerings. In every chapter, there are several examples and hands-on exercises that will show the reader how to combine Excel functions, add-ins, and connections to databases and to cloud services to reach the desired goal: building a full data analysis flow. Different machine learning models are shown, tailored to the type of data to be analyzed. At the end of the book, the reader is presented with some advanced use cases using Automated Machine Learning, and artificial neural network, which simplifies the analysis task and represents the future of machine learning.

  • Use Microsoft's product Excel to build advanced forecasting models using varied examples
  • Cover range of machine learning tasks such as data mining, data analytics, smart visualization, and more
  • Derive data-driven techniques using Excel plugins and APIs without much code required

Table of contents

1 Implementing Machine Learning Algorithms
Technical requirements
Understanding learning and models
Focusing on model features
Studying machine learning models in practice
Comparing underfitting and overfitting
Evaluating models
Summary
Questions
Further reading

2 Hands-On Examples of Machine Learning Models
Technical requirements
Understanding supervised learning with multiple linear regression
Understanding supervised learning with decision trees
Understanding unsupervised learning with clustering
Summary
Questions
Further reading

3 Importing Data into Excel from Different Data Sources
Technical requirements
Importing data from a text file
Importing data from another Excel workbook
Importing data from a web page
Importing data from Facebook
Importing data from a JSON file
Importing data from a database
Summary
Questions
Further reading

4 Data Cleansing and Preliminary Data Analysis
Technical requirements
Cleansing data
Visualizing data for preliminary analysis
Understanding unbalanced datasets
Summary
Questions
Further reading

5 Correlations and the Importance of Variables
Technical requirements
Building a scatter diagram
Calculating the covariance
Calculating the Pearson's coefficient of correlation
Studying the Spearman's correlation
Understanding least squares
Focusing on feature selection
Summary
Questions
Further reading

6 Data Mining Models in Excel Hands-On Examples
Technical requirements 
Learning by example – Market Basket Analysis
Learning by example – Customer Cohort Analysis
Summary
Questions
Further reading

7 Implementing Time Series
Technical requirements
Modeling and visualizing time series
Forecasting time series automatically in Excel
Studying the stationarity of a time series
Summary
Questions
Further reading

8 Visualizing Data in Diagrams, Histograms, and Maps
Technical requirements
Showing basic comparisons and relationships between variables
Building data distributions using histograms
Representing geographical distribution of data in maps
Showing data that changes over time
Summary
Questions
Further reading

9 Artificial Neural Networks
Technical requirements
Introducing the perceptron – the simplest type of neural network
Building a deep network
Understanding the backpropagation algorithm
Summary
Questions
Further reading

10 Azure and Excel - Machine Learning in the Cloud
Technical requirements
Introducing the Azure Cloud
Using AMLS for free – a step-by-step guide
Loading your data into AMLS
Creating and running an experiment in AMLS
Summary
Questions
Further reading

11 The Future of Machine Learning
Automatic data analysis flows
Automated machine learning
Summary
Questions
Further reading

 

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