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The Data Daily

What is Machine Learning?

What is Machine Learning?

Machine Learning (ML) is a technique that uses algorithms to learn from the data without being programmed explicitly. Due to the data abundance and efficient data storage, ML rose to the limelight in recent times, but the foundational research in this field was done in seventy’s and eighty’s. Different ways for a computer to learn from data — supervised learning, unsupervised learning, and reinforcement learning.

A supervised learning algorithm takes labeled data while training the model, and then the model makes predictions in the presence of the new data. These problems could be divided into regression and classification problems.

Unsupervised learning is when we are dealing with training data that is unlabelled that is without a target variable. The goal is to find patterns in the data to derive insights from it.

Two forms of unsupervised learning problems are Clustering and Association.

Reinforcement Learning(RL) is a type of machine learning technique that enables the agent to learn by trial and error using rewards and punishments as signals for positive and negative behavior. The goal is to find a suitable action model that would maximize the total cumulative reward of the agent.

Generally, reinforcement learning algorithms begin with a more explorative approach and as the reward systems are better understood, the algorithm will then lean towards exploitation.In the reinforcement problems, the act of reevaluating the probability in each state is known as a Markov Decision Process (MDP).An MDP consists of a set of finite environment states S ,a set of possible actions A(s) in each state, a real valued reward function R(s) and a transition model P(s’, s | a).

AlphaGoZero computer program uses RL to defeat a world champion in the ancient Chinese game of Go.

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