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What is machine learning and how it works


Machine Learning is a subset of AI uses computer algorithms to analyse data to make intelligent decision based on what it has learnt.Instead following the rule based algorithms machine learning build models to classify and prediction from data.Lets understand this by exploring the problem which can be solve with machine learning.

Suppose that a heart is going to fail or not can be solved by machine learning. So the answer is 'yes'.Let we are giving the data set like BPM (beats per minute),BMI (body mass index),age,sex and result.With machine learning we are able to learn and create models that predicts the result.Machine learning relies on defining the behavioral rules by examining and comparing large dataset to find the common patterns.

For example we can provide the machine learning program with large volume pictures of bird and can train the model to identify the bird label whenever bird input is provided.When a picture of bird is provided to the machine learning model.It will show the picture with some confidence this type of learning is called the supervised learning.

Supervised Learning-

An algorithm is trained on the human-labeled data.The more sample you provide the supervised learning algorithm,the more precise it becomes in classifying new data.

Unsupervised Learning-

Unsupervised Learning is another type of machine language which relies algorithm to unlabeled data and letting it find patterns by itself.You provide the input but not labels and let machine infern qualities that algorithm ingest unlabeled data,draws inferences and find patterns.This kind of learning is useful for clustering data where data is grouped how much similar to its neighbour and dissimilar to everything else.Once the data is clustered then different technique can be used to find patterns from
that data.

Reinforcement Learning-

Reinforcement Learning relies on providing a machine learning algorithm with a set of rules and constraints and letting it learn how to achieve its goal.You define the state,desired goal,allowed actions and constraints.The algorithm figures out how achieve the goal by trying different combination of allowed actions and it is rewarded or punish depending on whether the decision was the good one or not.Reinforcement learning can be use to play chess.

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