Book Image

Hands-On Machine Learning with Microsoft Excel 2019

By : Julio Cesar Rodriguez Martino
Book Image

Hands-On Machine Learning with Microsoft Excel 2019

By: Julio Cesar Rodriguez Martino

Overview of this book

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.
Table of Contents (17 chapters)
Free Chapter
1
Section 1: Machine Learning Basics
4
Section 2: Data Collection and Preparation
8
Section 3: Analytics and Machine Learning Models
11
Section 4: Data Visualization and Advanced Machine Learning

The Future of Machine Learning

Moving data analysis to the cloud is only one part of the way machine learning projects have changed over the last few years. Since the benefits of adding automation, Artificial Intelligence (AI), and machine learning to many different parts of business operations are now clear and don't need further proof, companies are now focused on more permanent solutions. In fact, the natural follow-up is to think about finished products that can complete the full data cycle, from data collection to visualization.

There are many ways to create data analysis flows that can consume data as it is created and return results and visualizations after applying machine learning models. Cloud services make this task easier and more efficient.

Automatic machine learning is the current tendency in data analysis, where several machine learning models can be tested...