Hands-On Machine Learning with Microsoft Excel 2019 Build Complete Data Analysis Flows, from Data Collection to Visualization
Hands-On Machine Learning with Microsoft Excel 2019 by Julio Cesar Rodriguez Martino is a practical guide published by Packt Publishing on April 30, 2019. This edition, comprising 254 pages, is designed for users of all levels who wish to leverage Excel for data preparation and machine learning tasks. The book provides a comprehensive overview of how to utilize Excel for building advanced forecasting models, applying machine learning techniques, and visualizing data analysis outcomes.
Readers will find step-by-step instructions on various aspects of machine learning projects, including data collection, model development, and result visualization using Excel’s features. The content covers essential topics such as data mining, data analytics, and smart visualization, with numerous examples and hands-on exercises to facilitate learning. Additionally, the book introduces advanced use cases involving Automated Machine Learning and artificial neural networks, making it suitable for data analysis enthusiasts and project managers looking to perform core machine learning tasks with minimal coding.
Official synopsis Publisher
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.
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