machine learning

Machine Learning is a vital sub-discipline of Artificial Intelligence. It enables systems to learn from data & feedback provided by humans. Opposed to the classical deterministic approach to software development machine learning allows us to build systems than learn new abilities and concepts very much like children do by observation and recognizing patterns.
Day-to-day examples of applied Machine Learning include recommendation engines of big web 
portals as well as facial recognition and self driving cars.

Why is Machine Learning Important?

Resurging 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 varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.

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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 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.

What's required to create good machine learning systems?

• Clearly defined business outcomes.

• Quality data and preparation capabilities.

• Advanced mathematical and algorithmic understanding.

• Strong subject matter expertise & industry knowledge

• Iterative & scalable processes

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.