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

Visualizing Data in Diagrams, Histograms, and Maps

If we are talking about machine learning, why should we care about visualization? The answer is simple: if you cannot show what you have analyzed and the outcome of your models to somebody without any technical knowledge, then you will not be able to show any added value. We have already shown how important data visualization is for understanding a dataset and to decide which features will be most useful for training our model. We are now going to investigate which type of diagram is best suited to tell the story of our data and the new information we got from it.

The following topics will be covered in this chapter:

  • Showing basic comparisons and relationships between variables
  • Building data distributions using histograms
  • Representing geographical distribution of data in maps
  • Showing data that changes over time
...