Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Microsoft Azure Machine Learning
  • Table Of Contents Toc
Microsoft Azure Machine Learning

Microsoft Azure Machine Learning

By : Sumit Mund
4 (5)
Buy this Book
close
close
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)
close
close
close
Preface
Icon
Preface
Icon
What this book covers
Icon
What you need for this book
Icon
Who this book is for
Icon
Conventions
Icon
Reader feedback
Icon
Customer support
Lock Free Chapter
1
1. Introduction
Icon
1. Introduction
Icon
Introduction to predictive analytics
Icon
Machine learning
Icon
Introduction to Azure Machine Learning
Icon
Summary
2
2. ML Studio Inside Out
Icon
2. ML Studio Inside Out
Icon
Introduction to ML Studio
Icon
Getting started with Microsoft Azure
Icon
Inside ML Studio
Icon
Workspace as a collaborative environment
Icon
Summary
3
3. Data Exploration and Visualization
Icon
3. Data Exploration and Visualization
Icon
The basic concepts
Icon
Data exploration in ML Studio
Icon
Do it yourself
Icon
Summary
4
4. Getting Data in and out of ML Studio
Icon
4. Getting Data in and out of ML Studio
Icon
Getting data in ML Studio
Icon
Data format conversion
Icon
Getting data from ML Studio
Icon
Summary
5
5. Data Preparation
Icon
5. Data Preparation
Icon
Data manipulation
Icon
Splitting data
Icon
Do it yourself
Icon
The Apply SQL Transformation module
Icon
Advanced data preprocessing
Icon
Summary
6
6. Regression Models
Icon
6. Regression Models
Icon
Understanding regression algorithms
Icon
Train, score, and evaluate
Icon
Linear regression
Icon
Optimizing parameters for a learner – the sweep parameters module
Icon
The train neural network regression – do it yourself
Icon
Comparing models with the evaluate model
Icon
Other regression algorithms
Icon
No free lunch
Icon
Summary
7
7. Classification Models
Icon
7. Classification Models
Icon
Understanding classification
Icon
Training, scoring, and evaluating modules
Icon
Classifying diabetes or not
Icon
Predicting adult income with decision-tree-based models
Icon
Do it yourself – comparing models to choose the best
Icon
Multiclass classification
Icon
Multiclass classification with the Iris dataset
Icon
Multiclass classification with the Wine dataset
Icon
Summary
8
8. Clustering
Icon
8. Clustering
Icon
Understanding the K-means clustering algorithm
Icon
Creating a K-means clustering model using ML Studio
Icon
Do it yourself
Icon
Clustering versus classification
Icon
Summary
9
9. A Recommender System
Icon
9. A Recommender System
Icon
The Matchbox recommender
Icon
Building a recommendation system
Icon
Summary
10
10. Extensibility with R and Python
Icon
10. Extensibility with R and Python
Icon
Introduction to R
Icon
Introduction to Python
Icon
Why should you extend through R/Python code?
Icon
Extending experiments using the Python language
Icon
Extending experiments using the R language
Icon
Summary
11
11. Publishing a Model as a Web Service
Icon
11. Publishing a Model as a Web Service
Icon
Preparing an experiment to be published
Icon
Creating a scoring experiment
Icon
Specifying the input and output of the web service
Icon
Publishing a model as a web service
Icon
Consuming a published web service
Icon
Web service configuration
Icon
Updating the web service
Icon
Summary
12
12. Case Study Exercise I
Icon
12. Case Study Exercise I
Icon
Problem definition and scope
Icon
The dataset
Icon
Data exploration and preparation
Icon
Model development
Icon
Model deployment
Icon
Summary
13
13. Case Study Exercise II
chevron up
Icon
13. Case Study Exercise II
Icon
Problem definition and scope
Icon
The dataset
Icon
Data exploration and preparation
Icon
Model development
Icon
Model deployment
Icon
Summary
14
Index
Icon
Index

Summary

In this last chapter, you solved another real-world problem. You started with understanding the problem and then, acquired the necessary data. After initial data exploration, you realized that the data has a large number of columns, so you used Python script modules to first split the data into two sets of features, and then used the PCA algorithm to get a reduced set of features. Then, you used the Filter Based Feature Selection module, which can identify most of the important features from the reduced dataset. To select the right model, you tried different algorithms and trained them with optimum parameters using the Sweep Parameters modules. Finally, you selected the model and proceeded to publish it as a web service.

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Microsoft Azure Machine Learning
End of Section 13
Next Section
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon