# Top 8 Machine Learning Algorithms in 2023 | Best Machine Learning Algorithms- Codexashish

Machine learning is a type of Artificial Intelligence that allows a software application to learn from the data and produce more accurate predicted outcomes without human intervention. A machine learning algorithm is a type of program code that enables professionals to analyze, study, and explore large complex datasets.

In this article, we are going to see the Top 8 Machine Learning Algorithms in detail. We are going to reveal the top 8 machine learning algorithms for beginners or
professionals. So, we will cover these algorithms in detail and we will discuss what is the main functionality of these algorithms. So without wasting any time let's start the article:-

## Types of Machine Learning

Machine Learning algorithms are classified into three types based on their learning techniques:-

## 1. Supervised Machine Learning

A supervised learning algorithm uses labeled data to predict outputs or results based on the input data. In supervised learning algorithm, data can be classified into a category or in a regression algorithm.

## 2. Unsupervised Machine Learning

An unsupervised learning algorithm uses unlabeled data to predict outputs or results. This algorithm first labels the unlabeled data by categorizing the data on the behalf of their shape, color, type, or form. This algorithm is used when we do not know the output or result.

## 3. Reinforcement Machine Learning

This algorithms learn from previous results, receive feedback after every step, and then decides whether to go ahead with the next step or not and then the system learns whether it made the right decision, wrong decision, or neutral decision in the process. Reinforcement Learning is basically used in self-driving car systems and automated systems.

## 1. Linear Regression

Linear regression is a supervised machine learning algorithm in which a relationship is established between dependent and independent variables by fitting them to a line that is known as the best fit of the line or the regression line.

## 2. Logistic regression

Logistic regression is a supervised learning algorithm that is used to estimate discrete values like binary values 0 or 1 from a set of independent variables. Logistic regression is widely used when the classification problem is binary- True/False, Yes/No, win or lose, positive or negative.

## 3. Decision Tree

This is a supervised machine learning algorithm that is used to split the problem into two or more homogeneous sets based on the most significant attributes/independent variables. This algorithm builds tree branches in a hierarchy approach and each branch can be considered as an if-else statement and the final classification or leaf node will be a final decision for the algorithm.

## 4. Support Vector Machines (SVMs)

The SVM algorithm is a classification algorithm method in which you represent the raw data as points in an n-dimensional space (where n is the number of features present). The value of each feature is then assigned to certain coordinates, making it easier to classify the data. Rows called classifiers can be used to separate and plot data.

## 5. Naive Bayes Algorithm

Naive Bayes demonstrates a probabilistic machine learning algorithm based on the Bayesian probability model and is used to solve classification problems. The main assumption of the algorithm is that the features considered are independent of each other and that changing the value of one does not affect the value of the other.

## 6. K-Means

K-Means is an unsupervised remote-based machine learning algorithm that performs clustering tasks. In this algorithm, you classify data sets into clusters (K-clusters), where data points in one set remain homogeneous and data points from two different clusters remain heterogeneous.

## 7. KNN Classification Algorithm

KNN algorithm is an unsupervised machine learning algorithm that solves a clustering problem. The data set is divided into a certain number of clusters in such a way that all data points in one cluster are homogeneous and heterogeneous to data in other clusters.

## 8. Random Forest Algorithm

A set of decision trees is known as a Random forest tree, this algorithm is like a forest in which decision trees are the many trees. To classify new object based on their attributes, each tree is classified and help in estimating missing data and tend to keep the accuracy intact in situations when a large chunk of data is missing in the dataset.

## Conclusion:

We have successfully covered the Top 8 Machine Learning Algorithms that most developers should know. If you want to become a Data Scientist or Data Analysis then this blog will help to learn these machine learning algorithms.