5 Machine Learning Algorithm you should know in 2023
Tags: machinelearning, artificialinteligence, alogoriothms, aiml
Published: 2022-11-15 11:51:04

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Hi, I'm **Anil Das** a Junior Software Engineer at **Luxoft India**. Here I would like to provide some useful machine-learning algorithms you should know.

Machine learning is a branch of Artificial Intelligence that enables computers to learn and make predictions based on data. Traditional AI focuses on programming computers to perform specific tasks, such as playing chess or recognizing faces. Machine learning is about giving computers the ability to learn new things and solve new problems without being explicitly programmed.
If you are reading this article, you might be one of those curious souls who asks ‘what if’ questions and likes puzzles. You may have heard about Artificial Intelligence (AI), but aren’t sure what it will mean for your career or how it could impact your life in the future. We know that probably sounds like a lot to take in, so don’t worry – we’ll break it down for you.

There are many different fields of artificial intelligence, each with its own set of challenges and techniques. Of all the branches of AI, machine learning has been getting a lot of attention recently. But what exactly is machine learning? It’s not as complicated as it might sound. It’s something that we use every day without even realizing it. 

Machine learning is simply a computer program that learns to perform a task instead of having the task programmed manually. The more you learn about machine learning, the more applications you will find for it in your day-to-day life and career.

Machine learning tasks can vary widely and the choice of algorithm will depend on things such as the size, dimensionality, and sparsity of the data. The target variable, the quality of the data, and the interactions and statistical relationships exist both within features and between the features and target variable. 

## 5 Machine Learning Algorithm:

## Linear Regression:
Linear regression is one of the most basic algorithms in machine learning. It’s a method for fitting a straight line to a set of data points. 
Given a set of predictor variables and a response variable, linear regression models the relationship between them by using the coefficients of a line to produce an estimate for the response, given the other variables in the model. 
Linear regression is commonly used to predict continuous outcomes, like price, or measured quantities, like the amount of CO2 emitted by a factory. 

## Logistic Regression:
Logistic regression is a type of supervised machine learning model that is used for binary outcomes. It can be used for any type of binary outcome, including things like whether or not someone will buy something, stay in a relationship, or get a particular disease. 
Logistic regression is very similar to linear regression but is used when the dependent variable is categorical (i.e., not numerical). Logistic regression is called “logistic” because its outcome is a logarithmic ratio between the independent variable and a prediction of the probability of the outcome. Logistic regression is useful when the outcome is rare and the data is noisy.

**Random Forest:**
The random forest algorithm is a type of machine learning algorithm that creates a “forest” of decision trees. Decision trees are a popular way to visualize what goes into a machine-learning model, but they can be difficult to implement. Random forests are an alternative to decision trees that is much easier to implement. 
The random forest approach uses many decision trees to create one model that can be used to make predictions. The algorithm builds a collection of decision trees and chooses which trees to use for prediction based on a sample of the data. 
Random forests can handle a wide range of data types and are flexible enough to allow you to make changes to the model without having to re-train it entirely. 

## K Means:
K-means is a clustering algorithm that groups a set of data points into “clusters” based on their similarity. It is often used to segment a customer base or a sales pipeline or identify anomalies in a set of data. K-means is an iterative algorithm, meaning it will cycle through a set of steps until a solution is found. 
The steps of the algorithm are: 
1) Pick several “clusters” (or groupings) to create. 
2) Assign each data point to the closest cluster. 
3) Move each data point closer to the center of the cluster with a slight random variation. 
4) Repeat until the solution is found. K-means is a good clustering algorithm to start with when you’re first getting into data science because it’s simple, straightforward, and usually gives good results with relatively clean data. 

## XGBoost:

The XGBoost algorithm is a machine learning algorithm that is designed for very large and sparse data sets. It can be used for both classification and ranking problems. XGBoost is an extension of the gradient boosting algorithm, which is used for many classification and regression tasks. You can use it with both categorical and numerical data. 
XGBoost has been popularized recently by tech companies like Google, and Facebook and Chinese tech companies like Baidu and Tencent. These are just a few examples of the many machine-learning algorithms you can use to solve problems. 
If you are new to machine learning, it can be helpful to focus on one type of problem first, then once you get more practice and experience, you can explore more algorithms and ways to solve a problem.

## Conclusion:
Here, we learned the five most useful machine-learning algorithms those are, what is Machine learning, Linear Regression, Logistic Regression, Random Forest, K Means, and XGBoost. I hope, this article gives you some knowledge about machine learning and some useful algorithm also. Thank you for reading and hope you have enjoyed it as well. Stay tuned for updates regarding my next articles (by subscribing). Feel free to comment with your questions.