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Hands-On Machine Learning with Microsoft Excel 2019
Wehavemadehugeprogressinteachingcomputerstoperformdifficulttasks,especiallythosethatarerepetitiveandtime-consumingforhumans.Excelusers,ofalllevels,canfeelleftbehindbythisinnovationwave.ThetruthisthatalargeamountoftheworkneededtodevelopanduseamachinelearningmodelcanbedoneinExcel.Thebookstartsbygivingageneralintroductiontomachinelearning,makingeveryconceptclearandunderstandable.Then,itshowseverystepofamachinelearningproject,fromdatacollection,readingfromdifferentdatasources,developingmodels,andvisualizingtheresultsusingExcelfeaturesandofferings.Ineverychapter,thereareseveralexamplesandhands-onexercisesthatwillshowthereaderhowtocombineExcelfunctions,add-ins,andconnectionstodatabasesandtocloudservicestoreachthedesiredgoal:buildingafulldataanalysisflow.Differentmachinelearningmodelsareshown,tailoredtothetypeofdatatobeanalyzed.Attheendofthebook,thereaderispresentedwithsomeadvancedusecasesusingAutomatedMachineLearning,andartificialneuralnetwork,whichsimplifiestheanalysistaskandrepresentsthefutureofmachinelearning.
目錄(161章)
倒序
- coverpage
- Title Page
- Copyright and Credits
- Hands-On Machine Learning with Microsoft Excel 2019
- Dedication
- About Packt
- Why subscribe?
- Packt.com
- Contributors
- About the author
- About the reviewer
- Packt is searching for authors like you
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Download the example code files
- Download the color images
- Conventions used
- Get in touch
- Reviews
- Section 1: Machine Learning Basics
- Implementing Machine Learning Algorithms
- Technical requirements
- Understanding learning and models
- Learning by example – the linear regression model
- Focusing on model features
- Studying machine learning models in practice
- Comparing underfitting and overfitting
- Evaluating models
- Analyzing classification accuracy
- Building the confusion matrix
- Calculating the Area Under Curve (AUC)
- Calculating the Mean Absolute Error (MAE)
- Calculating the Mean Squared Error (MSE)
- Summary
- Questions
- Further reading
- Hands-On Examples of Machine Learning Models
- Technical requirements
- Understanding supervised learning with multiple linear regression
- Understanding supervised learning with decision trees
- Deciding whether to train outdoors depending on the weather
- Entropy of the target variable
- Entropy of each feature with respect to the target variable
- Frequency table
- Entropy calculation
- Comparing the entropy differences (information gain)
- Understanding unsupervised learning with clustering
- Grouping customers by monthly purchase amount
- Summary
- Questions
- Further reading
- Section 2: Data Collection and Preparation
- 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
- Data Cleansing and Preliminary Data Analysis
- Technical requirements
- Cleansing data
- Visualizing data for preliminary analysis
- Understanding unbalanced datasets
- Summary
- Questions
- Further reading
- 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
- Section 3: Analytics and Machine Learning Models
- 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
- 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
- Section 4: Data Visualization and Advanced Machine Learning
- Visualizing Data in Diagrams Histograms and Maps
- Technical requirements
- Showing basic comparisons and relationships between variables
- The basic parts of an Excel diagram
- Column charts
- Combination charts
- Stacked charts
- Pie and bar charts
- Building data distributions using histograms
- Representing geographical distribution of data in maps
- Showing data that changes over time
- Summary
- Questions
- Further reading
- Artificial Neural Networks
- Technical requirements
- Introducing the perceptron – the simplest type of neural network
- Training a neural network
- Testing the neural network
- Building a deep network
- Understanding the backpropagation algorithm
- Summary
- Questions
- Further reading
- 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
- Creating a new experiment
- Training a decision tree model
- Making predictions with the model from Excel
- Summary
- Questions
- Further reading
- The Future of Machine Learning
- Automatic data analysis flows
- Data collection
- Data preparation
- Model training
- Unsupervised learning
- Visualizations
- Re-training of machine learning models
- Automated machine learning
- Summary
- Questions
- Further reading
- Assessment
- Chapter 1 Implementing Machine Learning Algorithms
- Chapter 2 Hands-On Examples of Machine Learning Models
- Chapter 3 Importing Data into Excel from Different Data Sources
- Chapter 4 Data Cleansing and Preliminary Data Analysis
- Chapter 5 Correlations and the Importance of Variables
- Chapter 6 Data Mining Models in Excel Hands-On Examples
- Chapter 7 Implementing Time Series
- Chapter 8 Visualizing Data in Diagrams Histograms and Maps
- Chapter 9 Artificial Neural Networks
- Chapter 10 Azure and Excel - Machine Learning in the Cloud
- Chapter 11 The Future of Machine Learning 更新時間：2021-06-24 15:11:32
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