Preface

Intelligent machines have been a dream of humankind for a very long time. Even if we are far from developing artificial general intelligence, we have made large progress in teaching computers to perform difficult tasks, especially those that are repetitive and time-consuming for humans.

Machine learning models can help any business to make sense of the available data, thus optimizing processes, lowering costs, and generally helping the business to plan ahead. Excel users, at all levels of ability, can feel left behind by this wave of innovation. Everybody is talking about R and Python as the only relevant tools for achieving these tasks. The truth is that a large amount of the work needed to develop and use a machine learning model can be done in Excel.

This book starts by giving a general introduction to machine learning, making the relevant concepts clear and understandable. It shows the reader every step of a machine learning project, from data collection and reading from different data sources, to developing the models and visualizing the results. In every chapter, there are several examples and hands-on exercises that show the reader how to combine Excel functions, add-ins, and connections to databases and cloud services to reach our desired goal: building a full data analysis flow. Different machine learning models are demonstrated and tailored to the type of data to be analyzed.

At the end of the book, the reader is presented with some advanced tools, like Azure Cloud and automated machine learning, which simplify the analysis task and represent the future of machine learning.

Who this book is for

This book is aimed at data analysts using Excel as their everyday tool, who need to go beyond Power Pivot and use add-ins and other advanced tools. Excel experts wanting to expand their knowledge to take advantage of the new connection possibilities between Excel and Azure will also benefit, as will project managers needing to test machine learning models without writing code.

It is generally taken for granted that, in order to do data science, from data cleansing to visualization and machine learning models, you need to be a Python or R programmer. This is not the case nowadays, and the general tendency seems to be heading toward code-free data science. The reader needs to learn that there are other options, avoiding code to take Excel to the next level and use it as a platform for professional data analysis and visualization.

What this book covers

Chapter 1, Implementing Machine Learning Algorithms, covers the basic machine learning algorithms and how to implement them.

Chapter 2, Hands-On Examples of Machine Learning Models, adds some examples of algorithms and their use cases.

Chapter 3, Importing Data into Excel from Different Data Sources, covers how to read data from different sources into Excel.

Chapter 4, Data Cleansing and Preliminary Data Analysis, describes data preprocessing to prepare data for use in machine learning models.

Chapter 5, Correlations and the Importance of Variables, covers feature engineering, which involves identifying redundant variables and useful relationships between variables.

Chapter 6, Data Mining Models in Excel Hands-On Examples, describes examples of the most frequently used algorithms in solving business problems such as Market Basket Analysis and customer cohort analysis.

Chapter 7, Implementing Time Series, covers time series analysis and prediction.

Chapter 8, Visualizing Data in Diagrams, Histograms, and Maps, describes the different available diagrams in Excel and what they are used for.

Chapter 9, Artificial Neural Networks, covers advances machine learning in the form of artificial neural networks and deep learning.

Chapter 10, Azure and Excel - Machine Learning in the Cloud, covers building and using machine learning models in the cloud, connecting them to Excel.

Chapter 11, The Future of Machine Learning, covers the automation of data analysis and predictive models.

To get the most out of this book

You will need working knowledge of Excel, including how to make cell calculations, input basic functions, and make diagrams. For Chapter 10, Azure and Excel - Machine Learning in the Cloud you will need a Microsoft account.

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