With the arrival of cloud computing and multi-core machines, we have enough compute capacity at our disposal to churn large volumes of data and dig out the hidden patterns contained in these mountains of data. This technology comes in handy, especially when handling Big Data. Today, companies collect and accumulate data at massive, unmanageable rates for website clicks, credit card transactions, GPS trails, social media interactions, and so on. And it is becoming a challenge to process all the valuable information and use it in a meaningful way. This is where machine learning algorithms come into the picture. These algorithms use all the collected “past” data to learn patterns and predict results or insights that help us make better decisions backed by actual analysis. You may have experienced various examples of machine learning in your daily life. Machine learning is used to build models from historical data, to forecast the future events with an acceptable level of reliability. This concept is known as predictive analytics. To get more accuracy in the analysis, we can also combine machine learning with other techniques such as data mining or statistical modeling. This progress in the field of machine learning is great news for the tech industry and humanity in general. But the downside is that there aren’t enough data scientists or machine learning engineers who understand these complex topics. Well, what if there was an easy to use a web service in the cloud, which could do most of the heavy lifting for us? What if it scaled dynamically based on our data volume and velocity? The answer is the new cloud service from Microsoft called Azure Machine Learning.
What You Will Learn
- Learn about Azure Machine Learning.
- Learn about various machine learning algorithms supported by Azure Machine Learning. - Learn how to build and run a machine learning experiment with real-world datasets.
- Learn how to use classification machine learning algorithms.
- Learn how to use regression machine learning algorithms.
- Learn how to expose the Azure ML machine learning experiment as a web service or API.
- Learn how to integrate the Azure ML machine learning experiment API with a web application
The following audience can go through the course: data science enthusiasts, software and IT engineers, statisticians, cloud engineers, software architects and technical and non-technical tech founders.
About The Author
Manuj Aggarwal: Manuj Aggarwal is an entrepreneur, investor, and technology enthusiast. Over the last few years, he has been a business owner, technical architect, CTO, coder, start up consultant, and more.
Currently, he is the principal consultant, architect, and CTO of a software consulting company, TetraNoodle Technologies, based in Vancouver, Canada. He works with various start-ups on a number of cutting edge and interesting problems. Whether it is ideation and the refining of your start up idea, or building a dream team to execute the idea, he provides a diverse set of solutions that help these start-ups to succeed in their plans.
He has been active in the software industry since 1997, and has worked with early-stage businesses through to Fortune 100 mega-corporations. He is passionate about sharing all the knowledge that he has acquired over the years. He is particularly interested in helping technical and non-technical entrepreneurs, founders, and co-founders of tech start-ups.
Table of contents
- Chapter 1 : Welcome and Introduction
- Chapter 2 : Get Familiar with Azure Machine Learning
- Chapter 3 : Deep Dive into Azure Machine Learning
- Chapter 4 : Predicting Housing Prices with Azure Machine Learning
- Chapter 5 : Deploy a Web Application with Azure Machine Learning
- Chapter 6 : Conclusion
- Title: Machine Learning In The Cloud With Azure Machine Learning
- Release date: April 2018
- Publisher(s): Packt Publishing
- ISBN: 9781789347524
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