Wee Hyong Tok's Books

Predictive Analytics with Microsoft Azure Machine Learning

Build and Deploy Actionable Solutions in Minutes

By: Valentine Fontama,Roger Barga,Wee Hyong Tok

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Predictive Analytics with Microsoft Azure Machine Learning

Build and Deploy Actionable Solutions in Minutes

By: Valentine Fontama,Roger Barga,Wee Hyong Tok

Data Science and Machine Learning are in high demand, as customers are increasingly looking for ways to glean insights from all their data. More customers now realize that Business Intelligence is not enough as the volume, speed and complexity of data now defy traditional analytics tools. While Business Intelligence addresses descriptive and diagnostic analysis, Data Science unlocks new opportunities through predictive and prescriptive analysis. The purpose of this book is to provide a gentle and instructionally organized introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book also provides a thorough overview of the Microsoft Azure Machine Learning service using task oriented descriptions and concrete end-to-end examples, sufficient to ensure the reader can immediately begin using this important new service. It describes all aspects of the service from data ingress to applying machine learning and evaluating the resulting model, to deploying the resulting model as a machine learning web service. Finally, this book attempts to have minimal dependencies, so that you can fairly easily pick and choose chapters to read. When dependencies do exist, they are listed at the start and end of the chapter. The simplicity of this new service from Microsoft will help to take Data Science and Machine Learning to a much broader audience than existing products in this space. Learn how you can quickly build and deploy sophisticated predictive models as machine learning web services with the new Azure Machine Learning service from Microsoft.

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Page Count

178

Publisher

Apress

Published Date

ISBN 10

148420445X

ISBN 13

9781484204450

Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition

By: Valentine Fontama,Roger Barga,Wee Hyong Tok

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Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition

By: Valentine Fontama,Roger Barga,Wee Hyong Tok

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. What’s New in the Second Edition? Five new chapters have been added with practical detailed coverage of: Python Integration – a new feature announced February 2015 Data preparation and feature selection Data visualization with Power BI Recommendation engines Selling your models on Azure Marketplace

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Page Count

303

Publisher

Apress

Published Date

ISBN 10

1484212002

ISBN 13

9781484212004

Practical Weak Supervision

By: Wee Hyong Tok,Amit Bahree,Senja Filipi

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Practical Weak Supervision

By: Wee Hyong Tok,Amit Bahree,Senja Filipi

Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive, leaving many companies with unfinished ML projects. There's a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi show you how to create products using weakly supervised learning models. You'll learn how to build natural language processing and computer vision projects using weakly labeled datasets from Snorkel, a spin-off from the Stanford AI Lab. Because so many companies have pursued ML projects that never go beyond their labs, this book also provides a guide on how to ship the deep learning models you build. Get up to speed on the field of weak supervision, including ways to use it as part of the data science process Use Snorkel AI for weak supervision and data programming Get code examples for using Snorkel to label text and image datasets Use a weakly labeled dataset for text and image classification Learn practical considerations for using Snorkel with large datasets and using Spark clusters to scale labeling

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Page Count

192

Publisher

"O'Reilly Media, Inc."

Published Date

ISBN 10

1492077011

ISBN 13

9781492077015

Practical Automated Machine Learning on Azure

Using Azure Machine Learning to Quickly Build AI Solutions

By: Deepak Mukunthu,Parashar Shah,Wee Hyong Tok

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Practical Automated Machine Learning on Azure

Using Azure Machine Learning to Quickly Build AI Solutions

By: Deepak Mukunthu,Parashar Shah,Wee Hyong Tok

Develop smart applications without spending days and weeks building machine-learning models. With this practical book, you’ll learn how to apply automated machine learning (AutoML), a process that uses machine learning to help people build machine learning models. Deepak Mukunthu, Parashar Shah, and Wee Hyong Tok provide a mix of technical depth, hands-on examples, and case studies that show how customers are solving real-world problems with this technology. Building machine-learning models is an iterative and time-consuming process. Even those who know how to create ML models may be limited in how much they can explore. Once you complete this book, you’ll understand how to apply AutoML to your data right away. Learn how companies in different industries are benefiting from AutoML Get started with AutoML using Azure Explore aspects such as algorithm selection, auto featurization, and hyperparameter tuning Understand how data analysts, BI professions, developers can use AutoML in their familiar tools and experiences Learn how to get started using AutoML for use cases including classification, regression, and forecasting.

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199

Publisher

O'Reilly Media

Published Date

ISBN 10

1492055565

ISBN 13

9781492055563

Introducing Windows Azure Hdinsight

By: Avkash Chauhan,Valentine Fontama,Michele Hart,Wee Hyong Tok,Buck Woody

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Introducing Windows Azure Hdinsight

By: Avkash Chauhan,Valentine Fontama,Michele Hart,Wee Hyong Tok,Buck Woody

Microsoft Azure HDInsight is Microsoft's 100 percent compliant distribution of Apache Hadoop on Microsoft Azure. This means that standard Hadoop concepts and technologies apply, so learning the Hadoop stack helps you learn the HDInsight service. At the time of this writing, HDInsight (version 3.0) uses Hadoop version 2.2 and Hortonworks Data Platform 2.0. In Introducing Microsoft Azure HDInsight, we cover what big data really means, how you can use it to your advantage in your company or organization, and one of the services you can use to do that quickly-specifically, Microsoft's HDInsight service. We start with an overview of big data and Hadoop, but we don't emphasize only concepts in this book-we want you to jump in and get your hands dirty working with HDInsight in a practical way. To help you learn and even implement HDInsight right away, we focus on a specific use case that applies to almost any organization and demonstrate a process that you can follow along with. We also help you learn more. In the last chapter, we look ahead at the future of HDInsight and give you recommendations for self-learning so that you can dive deeper into important concepts and round out your education on working with big data.

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Page Count

130

Publisher

Pearson Education

Published Date

ISBN 10

0735685517

ISBN 13

9780735685512

Deep Learning with Azure

Building and Deploying Artificial Intelligence Solutions on the Microsoft AI Platform

By: Mathew Salvaris,Danielle Dean,Wee Hyong Tok

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Deep Learning with Azure

Building and Deploying Artificial Intelligence Solutions on the Microsoft AI Platform

By: Mathew Salvaris,Danielle Dean,Wee Hyong Tok

Get up-to-speed with Microsoft's AI Platform. Learn to innovate and accelerate with open and powerful tools and services that bring artificial intelligence to every data scientist and developer. Artificial Intelligence (AI) is the new normal. Innovations in deep learning algorithms and hardware are happening at a rapid pace. It is no longer a question of should I build AI into my business, but more about where do I begin and how do I get started with AI? Written by expert data scientists at Microsoft, Deep Learning with the Microsoft AI Platform helps you with the how-to of doing deep learning on Azure and leveraging deep learning to create innovative and intelligent solutions. Benefit from guidance on where to begin your AI adventure, and learn how the cloud provides you with all the tools, infrastructure, and services you need to do AI. What You'll Learn Become familiar with the tools, infrastructure, and services available for deep learning on Microsoft Azure such as Azure Machine Learning services and Batch AI Use pre-built AI capabilities (Computer Vision, OCR, gender, emotion, landmark detection, and more) Understand the common deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs) with sample code and understand how the field is evolving Discover the options for training and operationalizing deep learning models on Azure Who This Book Is For Professional data scientists who are interested in learning more about deep learning and how to use the Microsoft AI platform. Some experience with Python is helpful.

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298

Publisher

Apress

Published Date

ISBN 10

1484236793

ISBN 13

9781484236796

Microsoft SQL Server 2012 Integration Services

By: Wee-Hyong Tok,Rakesh Parida,Matt Masson,Xiaoning Ding

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Microsoft SQL Server 2012 Integration Services

By: Wee-Hyong Tok,Rakesh Parida,Matt Masson,Xiaoning Ding

Build and manage data integration solutions with expert guidance from the Microsoft SQL Server Integration Services (SSIS) team. See best practices in action and dive deep into the SSIS engine, SSISDB catalog, and security features. Using the developer enhancements in SQL Server 2012 and the flexible SSIS toolset, you’ll handle complex data integration scenarios more efficiently—and acquire the skills you need to build comprehensive solutions. Discover how to: Use SSIS to extract, transform, and load data from multiple data sources Apply best practices to optimize package and project configuration and deployment Manage security settings in the SSISDB catalog and control package access Work with SSIS data quality features to profile, cleanse, and increase reliability Monitor, troubleshoot, and tune SSIS solutions with advanced features such as detailed views and data taps Load data incrementally to capture an easily consumable stream of insert, update, and delete activity

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Page Count

963

Publisher

Pearson Education

Published Date

ISBN 10

0735669910

ISBN 13

9780735669918

Book's by Wee Hyong Tok

Predictive Analytics with Microsoft Azure Machine Learning

Predictive Analytics with Microsoft Azure Machine Learning

Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition

Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition

Practical Weak Supervision

Practical Weak Supervision

Practical Automated Machine Learning on Azure

Practical Automated Machine Learning on Azure

Introducing Windows Azure Hdinsight

Introducing Windows Azure Hdinsight

Deep Learning with Azure

Deep Learning with Azure

Microsoft SQL Server 2012 Integration Services

Microsoft SQL Server 2012 Integration Services

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