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Machine learning algorithms are mostly used in data science. The data science and machine

Learning jobs have increased in the market over the years. Machine learning and data science jobs percentage is 30 percent in most of the countries. The data science jobs percentage will increase in the next few years. According to one report of the United States, the data science jobs will be 70 percent of the total jobs up to 2026. It is essential to know about machine learning and data science. Our team of experts has discussed some of the most used and prominent machine learning algorithms. We have also described these algorithms to some extent. Know more about Data Science Course in Pune

 

Machine Learning Algorithms:

 

Here are some of the most used and prominent machine learning algorithms:

 

1.Naive Bayes Classifier Algorithm:

 

The Bayesian theorem works on probability and is used to predict the different aspects of the

given problem. The algorithms use the given data and determine the required value using the Bayesian formula to determine the conditional probability. Naive Bayes algorithm can find out the probability using the formula to predict in which class a specific data item will fall. So the Naïve Bayes Classifier predicts the class of the data item using the probability technique. For example, we have a data set of both males and females. Suppose the data attributes are height, weight, and age. We have to find the data item class with given values of weight, height, and age. The naive Bayes classifier calculates the probability of a certain data item to select a class accordingly. The naive Bayes classifier deals with classification problems when the given data set has the numeric data, and you have to find out the class of the data item.

 

2. Linear Regression:

 

First of all, we need to know what linear regression is. Linear regression is a predictive technique used to explore the relationship between the dependent and independent variables. Let us consider an example for explaining the linear regression. Suppose we have a data set in which we have to predict the house price from the house data set's given attributes. The given attributes can be the number of rooms, location, number of stories in the house, and house price, the dependent variable. After data scientist training and placement in hyderabad the data set using the regression model, the new data's house price can be easily determined. But linear regression can not handle the categorical data and cannot solve the classification problems.

 

3. K Means Clustering:

 

K Means Clustering is the clustering algorithm, and the clustering problems belong to the

unsupervised machine learning type. The K means clustering algorithms deal with most of the clustering problems with accuracy. The K Means clustering algorithms find out each data item's distance with all other data items and place the data items with the minimum distance with each other into the same group or cluster. The K means clustering algorithm deals with numerical data type but can be used for text data type by making a few changes and modifications. Learn more Data Science Course in Chennai

 

We have discussed some mostly used and prominent algorithms in data science and machine learning with a few details. For more articles related to data science, please keep visiting our website.

 

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