In the next ten years, it is believed that machine learning algorithms will replace many jobs worldwide. The growth of data is upgrading rapidly. Self-modifying and automated information is evolving and progressing, which results in extensive automation of data.

The simplest example of the machine learning algorithm is the way computers play chess. The algorithm is designed to understand the player’s moves and play according to them. Before we get to the conclusion, let us know the basic idea of the machine learning algorithm.

**What is a machine learning algorithm? **

A machine learning algorithm, in simple terms, is defined as an approach that works and runs on data. A user can build a production-ready model with the help of machine learning.

In simple terms, if an airplane is using machine learning to accomplish a task, then the machine learning algorithm will be the engines inside of the plane which completes the job. Of course the use of machine learning has become vast, from our cars and favorite search engines to dating apps to meet milfs in your area.

There are three types of machine learning algorithms:

- Supervised: In this learning, the algorithms are given a set of samples for prediction. The algorithms use patterns within the label to predict the outputs. Examples of supervised learning are Linear Regression, Decision tree, KNN.
- Unsupervised: The data in this algorithm organize into a group of clusters. It then describes its structure. The learning algorithm then converts complex data into a simple and organized way for analysis. An example of unsupervised learning algorithms is K-means and Apriori algorithms.
- Reinforcement: with this algorithm, the machine is designed to make specific decisions. The device uses the trial and error met hid to train itself continuously. It learns from its past mistakes and captures knowledge to make correct decisions. An example of a reinforcement learning algorithm is the Markov decision process.

Some of the commonly used machine learning algorithms is as follows:

**Naïve Bayes Algorithm**

The algorithm is based on the Bayes theorem. It assumes that in a class, the presence of a specific feature is not related to any of the other components. The theorem means that the elements are all independent of each other.

The theorem is based on probabilities. It is one of the most popular algorithms as it is straightforward and easy to understand. The algorithm is efficient and valuable for large database sets.

**Linear Regression**

The Linear regression algorithms show the relationship between two variables. It also shows that when one variable has an impact, the other variable is also affected. The algorithm helps to estimate the real continuous value of the products.

Some of the common examples are sales predictions, employee salary estimation, and predicting housing prices.

When the independent and dependent variables fit into a line, a relationship is established. The line is known as the regression line. It is presented by the equation Y= a*X+b. Y is the dependent variable, x is the independent variable, a is the slope, and b is the intercept.

Linear regression is based on predictions and is made of two types; simple and multiple linear regressions. Single linear regression is made by one independent variable, and multiple is characterized by more than one variable.

**Decision tree**

The Machine learning algorithm; decision tree is one of the most used and popular algorithms. It helps in classifying problems. It is one of the most reliable and popular learning algorithms for the classification of issues.

Categorical and continuous dependent variables categorically work very well with the decision tree algorithm. The algorithm works by splitting homogeneous groups by taking remarkable attribute variables.

The learning algorithm makes a graphical representation of a tree and its branches.

A decision tree helps in capturing ideas if there is confusion on how to operate a situation.

**Logistic regression**

The algorithm performs classification of tasks, unlike linear regression that performs regression problems. It works by applying a logistic function to a variety of features. The algorithm then predicts the outcome of an attribute variable.

It is also called logit regression as it adds data to the logit function and predicts probability. The output value comes between 0 and 1.

**SVM(support vector machine) algorithm**

The learning algorithm works in a classification method. Each data is the plot in an n-dimensional space, where n is the number of features. The value of each feature variable is attached to a particular coordinate. It helps to make it easy to analyze the data. The line splits the data, which are called classifiers, and it then plots the features into the graph for analysis.

**KNN algorithm**

The KNN (k-nearest neighbor) algorithms work well for regression and classification problems. It is one of the most simple machine learning algorithms.

The algorithm stores all the available cases. It then classifies new topics by voting, the majority vote, of its K- neighbors. The most common class to the instances is then assigned, and a distance function performs the measurements.

A simple example to explain the algorithm is by comparing it with real life. If you want to buy vegetables, then you will have to look around in markets and shops. The shop with a variety of fresh vegetables and is nearest to your location is the one you will choose to purchase.