Accuracy depends on how we train the system. There are two ways for machines to learn something:

• Rule-based learning
• Pattern-based learning

In rule-based learning, the developer defines a bunch of rules and the machine parses the incoming data against those rules to come to a conclusion. This approach is good for monotonous systems and where things do not change that often.

What if we are trying to build ;intelligence for a weather prediction system? Will the learning that we have had up to today be enough for us to get an accurate prediction, even after 50 years? Maybe not.

This is where pattern-based learning comes in. Pattern-based learning is more popularly known as machine learning (ML). In today's world, most of the learning by computers happens through machine learning. Let's take a quick look at how ML plays an important role in this.