Machine learning is the branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Machine learning is a method of data analysis that automates analytical model building.
Machine learning is a specific subset of AI that trains a machine how to learn while artificial intelligence (AI) is the broad science of mimicking human abilities.
How Machine Learning Evolved
The advancement of new computing technologies, machine learning today is not like machine learning of the past. It started from pattern recognition with the theory that computers can learn to do certain tasks without being programmed; researchers interested in artificial intelligence wanted to see if computers could learn from data. The reprise facet of machine learning is crucial because as models are exposed to new data, they are able to independently adapt and learn from previous computations to produce reliable, iterate decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.
While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – repeatedly and faster – is a new development.
Here are a few widely publicized examples of machine learning applications you may have heard of :
Virtual Personal Assistants: Siri, Alexa, Google Now are some of the popular examples of virtual personal assistants
The hyped, self-driving Google car! The essence of machine learning.
Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life.
Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.
Fraud detection! One of the more obvious, important uses in our world today.
Machine Learning and Artificial Intelligence
While artificial intelligence (AI) is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn. Watch this video to better understand the relationship between AI and machine learning. You’ll see how these two technologies work, with useful examples and a few funny asides.
Why is machine learning important?
The upturn of interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and diversity of available data, computational processing that is cheaper and more powerful, and affordable data storage.
All of these things mean it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more precise results – even on a very large scale. And by building accurate models, an organization has a better chance of recognizing profitable opportunities – or avoiding unknown risks.
What's required to create good machine learning systems?
- Data preparation capabilities.
- Algorithms – basic and advanced.
- Automation and iterative processes.
- Ensemble modeling.
Did you know?
- In machine learning, a target is called a label.
- In statistics, a target is called a dependent variable.
- A variable in statistics is called a feature in machine learning.
- A transformation in statistics is called feature creation in machine learning.
Machine learning in today's world
By using algorithms to build models that uncover connections, organizations can make better decisions without human intervention. Learn more about the technologies that are shaping the world we live in.
Who's using it?
Most industries working with large amounts of data have recognized the value of machine learning technology. By gleaning insights from this data – often in real time – organizations are able to work more efficiently or gain an advantage over competitors.