Wee Hyong Tok

Predictive Analytics with Microsoft Azure Machine Learning

Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book provides a thorough overview of the Microsoft Azure Machine Learning service released for general availability on February 18th, 2015 with practical guidance for building recommenders, propensity models, and churn and predictive maintenance models. The authors use task oriented descriptions and concrete end-to-end examples to ensure that the reader can immediately begin using this new service. The book describes all aspects of the service from data ingress to applying machine learning, evaluating the models, and deploying them as web services. Learn how you can quickly build and deploy sophisticated predictive models with the new Azure Machine Learning from Microsoft. Whats New in the Second Edition? Five new chapters have been added with practical detailed coverage of: Python Integration a new feature announced February 2015Data preparation and feature selection Data visualization with Power BIRecommendation engines Selling your models on Azure Marketplace What youll learn A structured introduction to Data Science and its best practices An introduction to the new Microsoft Azure Machine Learning service, explaining how to effectively build and deploy predictive models Practical skills such as how to solve typical predictive analytics problems like propensity modeling, churn analysis, product recommendation, and visualization with Power BIA practical way to sell your own predictive models on the Azure Marketplace Who this book is for Data Scientists, Business Analysts, BI Professionals and Developers who are interested in expanding their repertoire of skill applied to machine learning and predictive analytics, as well as anyone interested in an in-depth explanation of the Microsoft Azure Machine Learning service through practical tasks and concrete applications. The reader is assumed to have basic knowledge of statistics and data analysis, but not deep experience in data science or data mining. Advanced programming skills are not required, although some experience with R programming would prove very useful.

Introduction to Statistical and Machine Learning Algorithms

This chapter will serve as a reference for some of the most commonly used algorithms in Microsoft Azure Machine Learning. We will provide a brief introduction to algorithms such as linear and logistic regression, k-means for clustering, decision trees, decision forests (random forests, boosted decision trees, and Gemini), neural networks, support vector machines, and Bayes point machines.

Building Customer Propensity Models

This chapter will provide a practical guide for building machine learning models. It focuses on buyer propensity models, showing how to apply the data science process to this business problem. Through a step-by-step guide, this chapter will explain how to apply key concepts and leverage the capabilities of Microsoft Azure Machine Learning for propensity modeling.

Integration with R

This chapter will introduce R and show how it is integrated with Microsoft Azure Machine Learning. Through simple examples, you will learn how to write and run your own R code when working with Azure Machine Learning. You will also learn the R packages supported by Azure Machine Learning, and how you can use them in the Azure Machine Learning Studio (ML Studio).

Integration with Python

This chapter shows you how to use Python in Azure Machine Learning (Azure ML). Using simple examples, you will learn how to integrate Python as part of an Azure ML experiment. This enables you to tap into the powerful capabilities offered by various Python libraries, such as NumPy, SciPy, pandas, scikit-learn, and many more, directly in an Azure ML experiment.

Building Predictive Maintenance Models

The leading manufacturers are now investing in predictive maintenance, which holds the potential to reduce cost yet increase margin and customer satisfaction. Though traditional techniques such as statistics and manufacturing have helped, the industry is still plagued by serious quality issues and the high cost of business disruption when components fail. Advances in machine learning offer a unique opportunity to improve customer satisfaction and reduce service downtime. This chapter shows how to build models for predictive maintenance using Microsoft Azure Machine Learning. Through examples we will demonstrate how you can use Microsoft Azure Machine Learning to build, validate, and deploy a predictive model for predictive maintenance.

Consuming and Publishing Models on Azure Marketplace

The Azure Machine Learning Studio makes it easy to create new predictive models. But what if there was a way to harness the power of machine learning without having to understand the data science behind it? This is the promise of the Azure Machine Learning Marketplace. Think of it as the place where you can go find “baked” APIs that solve interesting problems thanks to machine learning. This chapter will introduce this marketplace, showing existing solutions from Microsoft and its partners. We will also show step by step how you can sell your own predictive models on Azure Marketplace.

Customer Segmentation Models

In this chapter, you will learn how to build customer segmentation models in Microsoft Azure Machine Learning. Using a practical example, we will present a step-by-step guide to using Microsoft Azure Machine Learning to easily build segmentation models using k-means clustering. After the models have been built, you will learn how to perform validation and deploy it in production.

Visualizing Your Models with Power BI

Building predictive models is essential. However, explaining the results is just as important. Even an excellent predictive model can be seriously undermined by a failure to effectively communicate the results. Data visualization helps data scientists explain the results of predictive models to their stakeholders and end users. In this chapter, we show how you can share the results of your models through Power BI.

Introduction to Data Science

So what is data science and why is it so topical? Is it just another fad that will fade away after the hype? We will start with a simple introduction to data science, defining what it is, why it matters, and why it matters now. This chapter will highlight the data science process with guidelines and best practices. It will introduce some of the most commonly used techniques and algorithms in data science. And it will explore ensemble models, a key technology on the cutting edge of data science.