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Microsoft Azure Machine Learning
Sumit Mund
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- 212 pages
- English
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eBook - ePub
Microsoft Azure Machine Learning
Sumit Mund
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Information
Microsoft Azure Machine Learning
Table of Contents
Microsoft Azure Machine Learning
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Support files, eBooks, discount offers, and more
Why subscribe?
Free access for Packt account holders
Instant updates on new Packt books
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the color images of this book
Errata
Piracy
Questions
1. Introduction
Introduction to predictive analytics
Problem definition and scoping
Data collection
Data exploration and preparation
Model development
Model deployment
Machine learning
Types of machine learning problems
Classification
Regression
Clustering
Common machine learning techniques/algorithms
Linear regression
Logistic regression
Decision tree-based ensemble models
Neural networks and deep learning
Introduction to Azure Machine Learning
ML Studio
Summary
2. ML Studio Inside Out
Introduction to ML Studio
Getting started with Microsoft Azure
Microsoft account and subscription
Creating and managing ML workspaces
Inside ML Studio
Experiments
Creating and editing an experiment
Running an experiment
Creating and running an experiment – do it yourself
Workspace as a collaborative environment
Summary
3. Data Exploration and Visualization
The basic concepts
The mean
The median
Standard deviation and variance
Understanding a histogram
The box and whiskers plot
The outliers
A scatter plot
Data exploration in ML Studio
Visualizing an automobile price dataset
A histogram
The box and whiskers plot
Comparing features
A snapshot
Do it yourself
Summary
4. Getting Data in and out of ML Studio
Getting data in ML Studio
Uploading data from a PC
The Enter Data module
The Data Reader module
Getting data from the Web
Fetching a public dataset – do it yourself
Getting data from Azure
Data format conversion
Getting data from ML Studio
Saving a dataset on a PC
Saving results in ML Studio
The Writer module
Summary
5. Data Preparation
Data manipulation
Clean Missing Data
Removing duplicate rows
Project columns
The Metadata Editor module
The Add Columns module
The Add Rows module
The Join module
Splitting data
Do it yourself
The Apply SQL Transformation module
Advanced data preprocessing
Removing outliers
Data normalization
The Apply Math Operation module
Feature selection
The Filter Based Feature Selection module
The Fisher Linear Discriminant Analysis module
Data preparation beyond ready-made modules
Summary
6. Regression Models
Understanding regression algorithms
Train, score, and evaluate
The test and train dataset
Evaluating
The mean absolute error
The root mean squared error
The relative absolute error
The relative squared error
The coefficient of determination
Linear regression
Optimizing parameters for a learner – the sweep parameters module
The decision forest regression
The train neural network regression – do it yourself
Comparing models with the evaluate model
Comparing models – the neural network and boosted decision tree
Other regression algorithms
No free lunch
Summary
7. Classification Models
Understanding classification
Evaluation metrics
True positive
False positive
True negative
False negative
Accuracy
Precision
Recall
The F1 score
Threshold
Understanding ROC and AUC
Motivation for the matrix to consider
Training, scoring, and evaluating modules
Classifying diabetes or not
Two-class bayes point machine
Two-class neural network with parameter sweeping
Predicting adult income with decision-tree-based models
Do it yourself – comparing models to choose the best
Multiclass classification
Evaluation metrics – multiclass classification
Multiclass classification with the Iris dataset
Multiclass decision forest
Comparing models – multiclass decision forest and logistic regression
Multiclass classification with the Wine dataset
Multiclass neural network with parameter sweep
Do it yourself – multiclass decision jungle
Summary
8. Clustering
Understanding the K-means clustering algorithm
Creating a K-means clustering model using ML Studio
Do it yourself
Clustering versus classification
Summary
9. A Recommender System
The Matchbox recommender
Types of recommendations
Understanding the recommender modules
The Train Matchbox recommender
The number of traits
The number of recommendation algorithm iterations
The Score Matchbox recommender
The evaluate recommender
Building a recommendation system
Summary
10. Extensibility with R and Python
Introduction to R
Introduction to Python
Why should you extend through R/Python code?
Extending experiments using the Python language
Understanding the Execute Python Script module
Creating visualizations using...