Machine Learning In The Cloud With Azure Machine Learning [Video]

More Information
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
About

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.

Style and Approach

In this course, we will discuss Azure Machine Learning in detail. You will learn what features it provides and how it is used. We will explore how to process some real-world datasets and find some patterns in that dataset.

Features
  • Azure Machine Learning is a cloud-based data science and machine learning service which is easy to use and is robust and scalable like other Azure cloud services.
  • It provides visual and collaborative tools to create a predictive model which will be ready-to-consume on web services without worrying about the hardware or the VMs which perform the calculations.
  • The advantage of Azure ML is that it provides a UI-based interface and pre-defined algorithms that can be used to create a training model. And it also supports various programming and scripting languages like R and Python.
Course Length

2 hours 59 minutes

ISBN 9781789347524
Date Of Publication 24 Apr 2018

Authors

Manuj Aggarwal

Manuj Aggarwal, the author, is an entrepreneur, investor and a technology enthusiast. He like startups, business ideas, and high-tech anything. He like to work on hard problems and get his hands dirty with cutting-edge technologies. In the last few years, he has been a business owner, technical architect, CTO, coder, startup consultant, and more. Currently, he works as the principal consultant, architect and CTO of a software consulting company TetraNoodle Technologies based in Vancouver, Canada. They work with various startups on some cutting edge and interesting problems. Whether it is ideation and refining of your startup idea or building a dream team to execute on the idea, they provide a diverse set of solutions which help these startups succeed in their plans. Manuj has been in the software industry since 1997 and has worked with early stage businesses to Fortune 100 mega corporations. With proficiency in creating innovative architectures and solutions, he has emerged as a professional who knows how to balance these solutions against cost, schedule, function, quality, and other business considerations. He is particularly interested in helping technical and non-technical entrepreneurs, founders and co-founders of tech startups. He likes to bring courses which provide practical know-how and advice about designing, architecting, optimizing and executing your next big idea.