This is the code repository for Hands-On Machine Learning with Microsoft Excel 2019, published by Packt.

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.

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.

This book covers the following exciting features:

• 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

If you feel this book is for you, get your copy today!

All of the code is organized into folders. For example, Chapter02.

Following is what you need for this book: 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.

With the following software and hardware list you can run all code files present in the book (Chapter 1-11).

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

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.

Click here if you have any feedback or suggestions.