MACHINE LEARNING

Today, whether you realize it or not, machine learning is everywhere ? automated translation, image recognition, voice search technology, self-driving cars, and beyond.

Machine learning, is an automated process that enables machines to solve problems with little or no human input, and take actions based on past observations.

While artificial intelligence and machine learning are often used interchangeably, they are two different concepts.

AI is the broader concept – machines making decisions, learning new skills, and solving problems in a similar way to humans – whereas machine learning is a subset of AI that enables intelligent systems to autonomously learn new things from data.

How does Machine Learning works?

New input data is fed into the machine learning algorithm to test whether the algorithm works correctly. The prediction and results are then checked against each other.

If the prediction and results don’t match, the algorithm is re-trained multiple times until the data scientist gets the desired outcome.

Types of Machine Learning:

Supervised learning algorithms and supervised learning models make predictions based on labeled training data. Each training sample includes an input and a desired output. Unsupervised learning algorithms uncover insights and relationships in unlabeled data. In this case, models are fed input data but the desired outcomes are unknown, so they have to make inferences based on circumstantial evidence, without any guidance or training. Reinforcement learning is concerned with how a software agent (or computer program) ought to act in a situation to maximize the reward.