Learning machine learning? Six articles you don’t want to miss

Chief Product Officer, IBM Cloud Paks and VP Cloud Integration, IBM

Digital disruption has revolutionized the way we live and do business — and machine learning is the latest wave of that revolution. Whether it’s by remaking medicine with innovations from Watson Health or by transforming online shopping with intelligent recommendations, machine learning is remaking industries across the board. 

And yet we’re just at the beginning, which is why IBM works to provide the best possible tools in the industry for data scientists. We’re using internal machine learning to detect when models fall out of spec — and automatically retraining them when they do. We’re offering integrated model management so data scientists don’t need to change environments. And we’re platform-agnostic, which means addressing business goals with the same look and feel across deployment options.

You’ll get to all those issues in good time if you’re not there already. In the meantime, many of you are still working to understand the basics of the technology. Whether you’re just getting started with machine learning, or looking to hone your skills, these six articles bring insight to this crucial topic.

  1. Top 10 Machine Learning Use Cases: Part One: Its sometimes difficult to comprehend the sheer reach of machine learning. This technology is already impacting nearly every industry. Trendy examples of uses include fraud detection and self-driving cars, but the less obvious innovations can turn out to be the most exciting. 
  1. Visualizing High Dimensional Data in Augmented Reality: For the modern enterprise, the power to make sense of data can spell the difference between success and failure. In this article, machine learning combines with augmented reality to help visualize, understand and capitalize on valuable information. 
  1. A Practical Guide to Machine Learning: Understand, Differentiate, and Apply: This thoughtful article provides a brief history of machine learning and business analytics, followed by a guide for understanding how machine learning can help organizations thrive in a rapidly changing world. 
  1. How to Decide: Machine Learning and the Science of Choosing: Dive into decision automation as machine learning promises to help organizations choose among options by making sense of data and by nudging leaders away from their natural human biases. Bonus: A glimpse into a potential future for decision science. 
  1. Six Steps Up: From Zero to Data Science for the Enterprise: This step-by-step guide could be required reading for any enterprise looking to establish modern data practices and build a data organization. The smartest organizations recognize data as a precious resource, but making the most of that data means charting a careful course. 
  1. The Most Popular Language for Machine Learning Is…: Machine learning has excited and inspired developers all over the world, and many of them are wondering what it takes to join the revolution. This post breaks down what programming languages and skills are emerging as most important for aspiring machine learning professionals. 

We’re living through a cognitive revolution, and it can be tough to navigate the changes. Hopefully, these articles serve as guideposts on your journey into this fast-evolving field. 

Learn more about machine learning and how it can help your organization by visiting the IBM Machine Learning website.

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