Hands-On Machine Learning with Microsoft Excel 2019

A practical guide to getting the most out of Excel, using it for data preparation, applying machine learning models (including cloud services) and understanding the outcome of the data analysis.

Key Features

• 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

Book Description

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.

What you will learn

• 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

Who this book is for

This book is for data analysis, machine learning enthusiasts, project managers, and someone who doesn't want to code much for performing core tasks of machine learning. Each example will help you perform end-to-end smart analytics. Working knowledge of Excel is required.

Table of Contents

1. Implementing Machine Learning Algorithms
2. Hands-on examples of machine learning models
3. Importing Data into Excel from Different Data Sources
4. Data cleansing and preliminary data analysis
5. Correlations and the Importance of Variables
6. Data Mining Models in Excel Hands-On Examples
7. Implementing Time Series
8. Visualizing data in diagrams, histograms, and maps
9. Artificial Neural Networks
10. Azure and Excel - Machine Learning in the Cloud
11. The future of Machine Learning

Julio Cesar Rodriguez Martino is a machine learning (ML) and artificial intelligence (AI) platform architect, focusing on applying the latest techniques and models in these fields to optimize, automate, and improve the work of tax and accounting consultants. The main tool used in this practice is the MS Office platform, which Azure services complement perfectly by adding intelligence to the different tasks.

Julio's background is in experimental physics, where he learned and applied advanced statistical and data analysis methods. He also teaches university courses and provides in-company training on machine learning and analytics, and has a lot of experience leading data science teams.