Everything You’ve Always Wanted to Know About Machine Learning

What is Machine Learning?

SAS defines machine learning as “a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.”

Princeton University lecturer Rob Schapire puts it in simpler terms: “Machine learning studies computer algorithms for learning to do stuff. We might, for instance, be interested in learning to complete a task, or to make accurate predictions, or to behave intelligently. The learning that is being done is always based on some sort of observations or data, such as examples, direct experience, or instruction. So in general, machine learning is about learning to do better in the future based on what was experienced in the past.”

Why Machine Learning Matters

With the power of machine learning, says SAS, “it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.” This leads to improved decision-making capabilities independent of human intervention with applications in a broad range of industries, including financial services, government, healthcare, marketing and sales, oil and gas, and transportation.

Machine learning is so promising, in fact, that Business Insider recently declared it to be “a revolution as big as the internet or personal computers.” With a track record of world-changing developments including everything from Amazon product recommendations to Google’s self-driving car, machine learning has already changed the world and how we live in it.

Is Machine Learning for You?

Of course, machine learning studies aren’t for everyone. But if you possess an interest in and aptitude for computer science fundamentals and programming; probability and statistics; data modeling and evaluation;  and software engineering and system design, you may be suited for an in-demand career in this red-hot field.

The reality is, however, that if you want to “future-proof” your career, these subjects may be the key.

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