Fundamental introduction to Machine Learning

Machine Learning is sub field of computer science that links to a
a framework which enables the intelligent output for domain experts,
the output gives domain expert the hidden knowledge, missing link, unknown feature and why the certain variables links to each other.Many of the organization uses machine learning to gain insights and customer behaviour.
But it is not necessary that every domain expert is also the computer science expert, a good understanding of different types of machine learning algorithms would increases the performance of domain expert. Below is three most novice and fundamental types of categories of machine learning.

Regression:

In this category of machine learning the result always related to the dependent variable, in this technique of machine learning the output is always related to predict the true value of the dependent variable following are some famous regression algorithms.

1-Linear Regression

2-Decision Tree

3-Random Forest

4-Boosting

Classification

This category as name refers is used to map out or to sort out the different elements in data sets. This technique is used to classify according to elements and objects similarities or dissimilarities The algorithms which come under classification are

1-Logistic Regression

2-Naive Bayes

3-SVM

4-Neural Networks

5-Decision Tree

6-Random Forest

6-Boosting

Clustering:

This technique is not same as classification but this technique is used to divide the massive number of objects to different regions or zones and find out the similar data points among divided zones some of the basic classification algorithms ( Decision tree, Random forest, Boosting) also applicable in this category, K-means is most famous and important algorithm used for clustering

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