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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
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