**Autor: Julio Cesar Rodriguez Martino**

Broj strana: 254

ISBN broj: 9781789345377

Izdavač:
PACKT PUBLISHING

Godina izdanja: 2019.

Pregleda (30 dana / ukupno): **53 / 544**

Predlog za prevod

- 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

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.

- 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

**Table of contents**

**1 Implementing Machine Learning Algorithms **

Technical requirements

Understanding learning and models

Focusing on model features

Studying machine learning models in practice

Comparing underfitting and overfitting

Evaluating models

Summary

Questions

Further reading **2 Hands-On Examples of Machine Learning Models **

Technical requirements

Understanding supervised learning with multiple linear regression

Understanding supervised learning with decision trees

Understanding unsupervised learning with clustering

Summary

Questions

Further reading **3 Importing Data into Excel from Different Data Sources **

Technical requirements

Importing data from a text file

Importing data from another Excel workbook

Importing data from a web page

Importing data from Facebook

Importing data from a JSON file

Importing data from a database

Summary

Questions

Further reading **4 Data Cleansing and Preliminary Data Analysis **

Technical requirements

Cleansing data

Visualizing data for preliminary analysis

Understanding unbalanced datasets

Summary

Questions

Further reading **5 Correlations and the Importance of Variables **

Technical requirements

Building a scatter diagram

Calculating the covariance

Calculating the Pearson's coefficient of correlation

Studying the Spearman's correlation

Understanding least squares

Focusing on feature selection

Summary

Questions

Further reading **6 Data Mining Models in Excel Hands-On Examples **

Technical requirements

Learning by example – Market Basket Analysis

Learning by example – Customer Cohort Analysis

Summary

Questions

Further reading **7 Implementing Time Series **

Technical requirements

Modeling and visualizing time series

Forecasting time series automatically in Excel

Studying the stationarity of a time series

Summary

Questions

Further reading **8 Visualizing Data in Diagrams, Histograms, and Maps **

Technical requirements

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

Summary

Questions

Further reading **9 Artificial Neural Networks **

Technical requirements

Introducing the perceptron – the simplest type of neural network

Building a deep network

Understanding the backpropagation algorithm

Summary

Questions

Further reading **10 Azure and Excel - Machine Learning in the Cloud **

Technical requirements

Introducing the Azure Cloud

Using AMLS for free – a step-by-step guide

Loading your data into AMLS

Creating and running an experiment in AMLS

Summary

Questions

Further reading **11 The Future of Machine Learning **

Automatic data analysis flows

Automated machine learning

Summary

Questions

Further reading

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