Book Image

Hands-On Machine Learning with Microsoft Excel 2019

By : Julio Cesar Rodriguez Martino
Book Image

Hands-On Machine Learning with Microsoft Excel 2019

By: Julio Cesar Rodriguez Martino

Overview of this book

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.

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Table of Contents (17 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Section 1: Machine Learning Basics
Section 1: Machine Learning Basics
2
Implementing Machine Learning Algorithms
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
3
Hands-On Examples of Machine Learning Models
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
4
Section 2: Data Collection and Preparation
Section 2: Data Collection and Preparation
5
Importing Data into Excel from Different Data Sources
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
6
Data Cleansing and Preliminary Data Analysis
Data Cleansing and Preliminary Data Analysis
Technical requirements
Cleansing data
Visualizing data for preliminary analysis
Understanding unbalanced datasets
Summary
Questions
Further reading
7
Correlations and the Importance of Variables
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
8
Section 3: Analytics and Machine Learning Models
Section 3: Analytics and Machine Learning Models
9
Data Mining Models in Excel Hands-On Examples
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
10
Implementing Time Series
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
11
Section 4: Data Visualization and Advanced Machine Learning
Section 4: Data Visualization and Advanced Machine Learning
12
Visualizing Data in Diagrams, Histograms, and Maps
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
13
Artificial Neural Networks
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
14
Azure and Excel - Machine Learning in the Cloud
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
15
The Future of Machine Learning
The Future of Machine Learning
Automatic data analysis flows
Automated machine learning
Summary
Questions
Further reading
16
Assessment
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

Understanding the backpropagation algorithm

There are two phases in the training process of a deep neural network: forward and back propagation. We have seen the forward phase in detail:

  1. Calculate the weighted sum of the inputs:
  1. Apply the activation function to the result:
Find different activation functions in the suggested reading at the end of the chapter. The sigmoid function is the most common and is easier to use, but not the only one.
  1. Calculate the output by adding all the results from the last layer (N neurons):

After the forward phase, we calculate the error as the difference between the output and the known target value: Error = (Output-y)2.

All weights are assigned random values at the beginning of the forward phase.

The output, and therefore the error, are functions of the weights wi and θi. This means that we could go backward from the error and see...

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