# Machine Learning [A to Z]

Machine Learning evolved from computer science that primarily studies the design of algorithms that can learn from experience. To learn, they need data that has certain attributes based on which […]

Machine Learning evolved from computer science that primarily studies the design of algorithms that can learn from experience. To learn, they need data that has certain attributes based on which the algorithms try to find some meaningful predictive patterns. Majorly, ML tasks can be categorized as concept learning, clustering, predictive modeling, etc. The ultimate goal of ML algorithms is to be able to take decisions without any human intervention correctly. Predicting the stocks or weather are a couple of applications of machine learning algorithms.

There are various machine learning algorithms like Decision trees, Naive Bayes, Random forest, Support vector machine, K-nearest neighbor, K-means clustering, etc.

From the class of machine learning algorithms, the one that you will be using today is k-nearest neighbor.

Now, the question is what exactly is K-Nearest Neighbor algorithm, so let us find out!

Machine Learning evolved from computer science that primarily studies the design of algorithms that can learn from experience. To learn, they need data that has certain attributes based on which the algorithms try to find some meaningful predictive patterns. Majorly, ML tasks can be categorized as concept learning, clustering, predictive modeling, etc. The ultimate goal of ML algorithms is to be able to take decisions without any human intervention correctly. Predicting the stocks or weather are a couple of applications of machine learning algorithms.

There are various machine learning algorithms like Decision trees, Naive Bayes, Random forest, Support vector machine, K-nearest neighbor, K-means clustering, etc.

From the class of machine learning algorithms, the one that you will be using today is k-nearest neighbor.

Now, the question is what exactly is K-Nearest Neighbor algorithm, so let us find out!

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