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

Studying machine learning models in practice

We have already seen a very simple example and used it to explain some basic concepts. In the next chapter, we are going to explore more complex models. We restricted ourselves to a very small dataset, just for clarity and to start our journey towards mastering machine learning with an easy task. There are some general considerations that we need to be aware of when working with machine learning models to solve real problems:

  • The amount of data is usually very large. In fact, a larger dataset helps to get a more accurate model and a more reliable prediction. Extremely large datasets, usually called big data, can present storage and manipulation challenges.
  • Data is never clean and ready to use, so data cleansing is extremely important and takes a lot of time.
  • The number of features required to correctly represent a real-life problem...