What is Machine Learning?

 

Typing “what is machine learning?” into a Google search opens up a pandora’s box of forums, academic research, and false information – and the purpose of this article is to simplify the definition and understanding of machine learning thanks to the direct help from our panel of machine learning researchers.
At Emerj, the AI Research and Advisory Company, many of our enterprise clients feel as though they should be investing in machine learning projects, but they don’t have a strong grasp of what it is. We often direct them to this resource to get them started with the fundamentals of machine learning in business.
In addition to an informed, working definition of machine learning (ML), we detail the challenges and limitations of getting machines to ‘think,’ some of the issues being tackled today in deep learning (the frontier of machine learning), and key takeaways for developing machine learning applications for business use-cases.
This article will be broken up into the following sections:

• What is machine learning?

• How we arrived at our definition (IE: the perspective of expert researchers)

• Machine learning basic concepts

• Visual representation of ML models

• How we get machines to learn

• An overview of the challenges and limitations of ML

• Brief introduction to deep learning

• Works cited

• Related ML interviews on Emerj

We put together this resource to help with whatever your area of curiosity about machine learning – so scroll along to your section of interest, or feel free to read the article in order, starting with our machine learning definition below:
What is Machine Learning?
* “Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.”
The above definition encapsulates the ideal objective or ultimate aim of machine learning, as expressed by many researchers in the field. The purpose of this article is to provide a business-minded reader with expert perspective on how machine learning is defined, and how it works. Machine learning and artificial intelligence share the same definition in the minds of many however, there are some distinct differences readers should recognize as well. References and related researcher interviews are included at the end of this article for further digging.
* How We Arrived at Our Definition:
(Our aggregate machine learning definition can be found at the beginning of this article)
As with any concept, machine learning may have a slightly different definition, depending on whom you ask. We combed the Internet to find five practical definitions from reputable sources:

1. “Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.” – Nvidia 

1. “Machine learning is the science of getting computers to act without being explicitly programmed.” – Stanford

1. “Machine learning is based on algorithms that can learn from data without relying on rules-based programming.”- McKinsey & Co.

1. “Machine learning algorithms can figure out how to perform important tasks by generalizing from examples.” – University of Washington

1. “The field of Machine Learning seeks to answer the question “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?” – Carnegie Mellon University

We sent these definitions to experts whom we’ve interviewed and/or included in one of our past research consensuses, and asked them to respond with their favorite definition or to provide their own. Our introductory definition is meant to reflect the varied responses. Below are some of their responses: