Explore predictive analytics using step-by-step tutorials and build models to make prediction in a jiffy with a few mouse clicks
About This Book
- Learn how to build predictive models using a browser such as IE
- Explore different machine learning algorithms available
- Without any prior knowledge and experience get started with predictive analytics with confidence
Who This Book Is For
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.
This book provides you with the skills necessary to get started with Azure Machine Learning to build predictive models as quickly as possible, in a very intuitive way, whether you are completely new to predictive analysis or an existing practitioner.
The book starts by exploring ML Studio, the browser-based development environment, and explores the first step―data exploration and visualization. You will then build different predictive models using both supervised and unsupervised algorithms, including a simple recommender system. The focus then shifts to learning how to deploy a model to production and publishing it as an API.
The book ends with a couple of case studies using all the concepts and skills you have learned throughout the book to solve real-world problems.
Table of Contents
Chapter 1. Introduction
Chapter 2. ML Studio Inside Out
Chapter 3. Data Exploration and Visualization
Chapter 4. Getting Data in and out of ML Studio
Chapter 5. Data Preparation
Chapter 6. Regression Models
Chapter 7. Classification Models
Chapter 8. Clustering
Chapter 9. A Recommender System
Chapter 10. Extensibility with R and Python
Chapter 11. Publishing a Model as a Web Service
Chapter 12. Case Study Exercise I
Chapter 13. Case Study Exercise II
- Author: Sumit Mund
- Pages: 173 pages
- Edition: 1
- Publication Date: 2015-03-31
- Publisher: Packt Publishing
- Language: English
- ISBN-10: 1784390798
- ISBN-13: 9781784390792
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