5 Types of Machine Learning Algorithms You Should Know

Machine learning has become a diverse business tool to enhance the various elements of business operations. Also, it has a significant influence on the performance of the business. Machine learning algorithms are used widely to maintain competition with different industries. However, there is a different type of algorithms for goals and data sets. The selection of an algorithm depends on user role and the purpose. If you are using Linear regression, then you can quickly implement or train rather than other machine learning algorithms. But the drawback of this algorithm is that it is not applicable for complex predictions. So you should know about the different types of machine learning algorithms for getting better results.

In this article, I am going to share five types of machine learning algorithms which enhance business success. These algorithms are user-friendly and support different goals. Besides, all of them are popular and used by thousands of enterprises.

Linear Regression

Linear regression is the pure form of algorithm which correlates between two variables in the data set. The input and output sets examined to show a relationship. It also shows how the change in one variable can affect the other variable. It is represented by plotting a line on the graph. The algorithm is popular because it is easy to explain, transparent, and requires no tuning. Companies use this algorithm to forecast sales and risk assessment to take long term business decisions.

Decision Tree

A decision tree consists of various branches which represent the outcome of many decisions. In this algorithm, the data is collected and graphed in multiple branches. It predicts response variables based on past decisions. This method communicated efficiently and visual for mapping out decisions and results. This algorithm work best for small data sets and low-stake decisions because of their long tail visuals.The popularity of this algorithm is due to the ability to show multiple outcomes and tests without any involvement of data scientists. If you need to check whether the decision traverse will affect the result or not, then this algorithm is best.

Support vector Machine

This algorithm is also known as SVM and internally analyzes the data set into classes. It is a helpful approach to future classifications. Then the main work of SVM is to find the line that separates training data into particular classes. Also, it maximizes the margins to enter future data into classes.The algorithm works best for training data, but nonlinear data can also be programmed into nonlinear SVMs. It is usually found in the financial sector because of its accuracy in current and future data sets. These algorithms are used widely for comparing gain in financial investment, values, and performance.

Apriori

Apriori algorithm is based on the principle of Apriori and is used in market analysis. This algorithm checks for the positive and negative correlation between products after analyzing the A and B in data sets. It is specially used by sales teams who keep an eye on the baskets of customers to find which products the customers will purchase with other products. For example, if most of the customers are buying A (bread) with B (butter), then this relation holds a high percentage. Also, it will conclude that Purchase of A will often lead to B. It will refer information in data sets and purchase ratios.Moreover, this algorithm informs marketing intentions as well as product placement strategies. The top-ranked companies, including Alibaba, Amazon, and Google, are using this algorithm to predict searches and product purchases.

K-means clustering

K- means clustering algorithms sorts of data sets through defined clusters. It is an iterative method which also put out similar groups with input data attached. For example, If you use K- means algorithm for sorting web results for word civil, then it will show the results in the form of groups. Accuracy is the main advantage of this algorithm. It has developed a reputation for providing the streamlined groupings in a short time as compared to other algorithms. It gives meaningful groups based on internal patterns. This algorithm helps marketers to identify target audience groups.

Final Words

Machine learning algorithms are available in different forms. Different users use these for distinct purposes so it is better to learn about different types of machine learning Algorithms. I hope this article has helped you in getting knowledge about ML algorithms.