Hands-On Machine Learning with Microsoft Excel 2019电子书

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Preface

Who this book is for

What this book covers

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Section 1: Machine Learning Basics

Implementing Machine Learning Algorithms

Technical requirements

Understanding learning and models

Learning by example – the linear regression model

Focusing on model features

Studying machine learning models in practice

Comparing underfitting and overfitting

Evaluating models

Analyzing classification accuracy

Building the confusion matrix

Calculating the Area Under Curve (AUC)

Calculating the Mean Absolute Error (MAE)

Calculating the Mean Squared Error (MSE)

Summary

Questions

Further reading

Hands-On Examples of Machine Learning Models

Technical requirements

Understanding supervised learning with multiple linear regression

Understanding supervised learning with decision trees

Deciding whether to train outdoors depending on the weather

Entropy of the target variable

Entropy of each feature with respect to the target variable

Frequency table

Entropy calculation

Comparing the entropy differences (information gain)

Understanding unsupervised learning with clustering

Grouping customers by monthly purchase amount

Summary

Questions

Further reading

Section 2: Data Collection and Preparation

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

Data Cleansing and Preliminary Data Analysis

Technical requirements

Cleansing data

Visualizing data for preliminary analysis

Understanding unbalanced datasets

Summary

Questions

Further reading

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

Section 3: Analytics and Machine Learning Models

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

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

Section 4: Data Visualization and Advanced Machine Learning

Visualizing Data in Diagrams, Histograms, and Maps

Technical requirements

Showing basic comparisons and relationships between variables

The basic parts of an Excel diagram

Column charts

Combination charts

Stacked charts

Pie and bar charts

Building data distributions using histograms

Representing geographical distribution of data in maps

Showing data that changes over time

Summary

Questions

Further reading

Artificial Neural Networks

Technical requirements

Introducing the perceptron – the simplest type of neural network

Training a neural network

Testing the neural network

Building a deep network

Understanding the backpropagation algorithm

Summary

Questions

Further reading

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

Creating a new experiment

Training a decision tree model

Making predictions with the model from Excel

Summary

Questions

Further reading

The Future of Machine Learning

Automatic data analysis flows

Data collection

Data preparation

Model training

Unsupervised learning

Visualizations

Re-training of machine learning models

Automated machine learning

Summary

Questions

Further reading

Assessment

Chapter 1, Implementing Machine Learning Algorithms

Chapter 2, Hands-On Examples of Machine Learning Models

Chapter 3, Importing Data into Excel from Different Data Sources

Chapter 4, Data Cleansing and Preliminary Data Analysis

Chapter 5, Correlations and the Importance of Variables

Chapter 6, Data Mining Models in Excel Hands-On Examples

Chapter 7, Implementing Time Series

Chapter 8, Visualizing Data in Diagrams, Histograms, and Maps

Chapter 9, Artificial Neural Networks

Chapter 10, Azure and Excel - Machine Learning in the Cloud

Chapter 11, The Future of Machine Learning