Julio Cesar Rodriguez Martino - Hands-On Machine Learning with Microsoft Excel 2019: Build complete data analysis flows, from data collection to visualization

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Julio Cesar Rodriguez Martino is a machine learning (ML) and artificial intelligence (AI) platform architect, focusing on applying the latest techniques and models i n 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 a dding 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.

Shashidhar Soppin is a senior software architect with more than 18 years' experience in information technology. He has worked on virtualization, storage, the cloud and cloud architecture, OpenStack, machine learning, deep learning, and Docker container technologies. Primarily his focus is on building new approaches and solutions for Enterprise customers. He is avid author of open source technologies (OSFY), a blogger (LinuxTechi), and a holder of patents. He graduated from BIET, Davangere. In his free time, he loves to travel and read books.

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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.

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.