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  • Book Overview & Buying Microsoft Azure Machine Learning
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Microsoft Azure Machine Learning

Microsoft Azure Machine Learning

By : Sumit Mund
4 (5)
Buy this Book
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Microsoft Azure Machine Learning

Microsoft Azure Machine Learning

4 (5)
By: Sumit Mund
Buy this Book

Overview of this book

The book is intended for those who want to learn how to use Azure Machine Learning. Perhaps you already know a bit about Machine Learning, but have never used ML Studio in Azure; or perhaps you are an absolute newbie. In either case, this book will get you up-and-running quickly.
Table of Contents (15 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Introduction
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1. Introduction
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Introduction to predictive analytics
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Machine learning
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Introduction to Azure Machine Learning
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Summary
2
2. ML Studio Inside Out
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2. ML Studio Inside Out
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Introduction to ML Studio
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Getting started with Microsoft Azure
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Inside ML Studio
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Workspace as a collaborative environment
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Summary
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3. Data Exploration and Visualization
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3. Data Exploration and Visualization
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The basic concepts
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Data exploration in ML Studio
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Do it yourself
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Summary
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4. Getting Data in and out of ML Studio
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4. Getting Data in and out of ML Studio
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Getting data in ML Studio
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Data format conversion
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Getting data from ML Studio
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Summary
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5. Data Preparation
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5. Data Preparation
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Data manipulation
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Splitting data
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Do it yourself
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The Apply SQL Transformation module
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Advanced data preprocessing
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Summary
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6. Regression Models
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6. Regression Models
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Understanding regression algorithms
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Train, score, and evaluate
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Linear regression
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Optimizing parameters for a learner – the sweep parameters module
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The train neural network regression – do it yourself
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Comparing models with the evaluate model
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Other regression algorithms
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No free lunch
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Summary
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7. Classification Models
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7. Classification Models
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Understanding classification
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Training, scoring, and evaluating modules
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Classifying diabetes or not
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Predicting adult income with decision-tree-based models
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Do it yourself – comparing models to choose the best
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Multiclass classification
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Multiclass classification with the Iris dataset
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Multiclass classification with the Wine dataset
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Summary
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8. Clustering
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8. Clustering
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Understanding the K-means clustering algorithm
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Creating a K-means clustering model using ML Studio
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Do it yourself
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Clustering versus classification
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Summary
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9. A Recommender System
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9. A Recommender System
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The Matchbox recommender
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Building a recommendation system
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Summary
10
10. Extensibility with R and Python
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10. Extensibility with R and Python
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Introduction to R
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Introduction to Python
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Why should you extend through R/Python code?
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Extending experiments using the Python language
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Extending experiments using the R language
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Summary
11
11. Publishing a Model as a Web Service
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11. Publishing a Model as a Web Service
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Preparing an experiment to be published
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Creating a scoring experiment
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Specifying the input and output of the web service
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Publishing a model as a web service
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Consuming a published web service
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Web service configuration
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Updating the web service
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Summary
12
12. Case Study Exercise I
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12. Case Study Exercise I
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Problem definition and scope
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The dataset
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Data exploration and preparation
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Model development
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Model deployment
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Summary
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13. Case Study Exercise II
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13. Case Study Exercise II
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Problem definition and scope
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The dataset
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Data exploration and preparation
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Model development
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Model deployment
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Summary
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Index
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Index

Model development

After preparing your data, you are not sure which model or regression algorithm would perform well for the problem at hand. Because the target variable P is continuous, you know that it's a regression problem. So, it would be worthwhile trying different algorithms and choosing the best one. You may use the Sweep Parameters module to obtain the optimum parameters for the algorithm. You need to pass three inputs to the Sweep Parameters module: the untrained algorithm, training dataset, and validation dataset. Use the Score modules to score the test data. Use an Evaluate module to compare the two models with the scored data.

You should try different algorithms to choose the best one. The following figure is just for your reference, which shows four algorithms.

Model development

Run the model and find out which algorithm performs the best for you.

CONTINUE READING
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Tech Concepts
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Programming languages
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Microsoft Azure Machine Learning
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