In this era of continuous technological innovation, organizations increasingly depend on intelligent business solutions generated through machine learning algorithms. From identifying hidden patterns to predicting future trends and uncertainties, machine learning algorithms have played a crucial role in how organizations operate today. Machine learning (ML) is essential for creating Artificial Intelligence (AI) solutions. Modern businesses use a combination of AI, ML, Big Data Analytics, and Business Intelligence (BI) to attain sustainable business growth. However, companies can only derive meaningful information from the data collected and stored in their information systems with algorithms used in machine learning.

**Machine Learning Algorithms**

Organizations use different machine learning algorithms to solve their business problems. ML algorithms are based on predictive analysis or predictive modeling. Thus, they are automated and have self-modifying features that evolve with experience and time. Data scientists and analysts use these algorithms or programs to learn from past experiences and produce the desired results without human intervention. Since all problems cannot be solved by one machine learning algorithm, data scientists use multiple algorithms to test and find the appropriate solutions. Hence, ML algorithms aim to generate as many accurate predictions as possible.

*Machine learning algorithms can be divided into three types depending on the ML models used. These are:*

**Supervised Learning**– The algorithms use a mapping function to connect an input to an output among input-output pairs in this model. Supervised learning is further divided into two: classification and regression.**Unsupervised Learning**– In an unsupervised learning model, algorithms do not have a fixed structure. Therefore, they are used to find hidden patterns. Clustering and dimensionality reduction are two types of unsupervised learning.**Reinforcement Learning**– Here, the algorithms used are encouraged to interact with the environment and derive results using a trial-and-error method. As a result, machines learn from the environment they are engaging with and find the best possible solutions.

**10 Popular ML Algorithms**

*Here are the ten most used algorithms in machine learning:*

**Linear Regression**– This is the most commonly used machine learning algorithm that data scientists use for predictive analysis. This algorithm is also used in statistics; linear regression has proven its usefulness in determining the relationship between two variables. The linear regression equation represents a line that best defines the relationship between the input variables (x) and the output variables (y). The goal is to quantify this relationship by finding the values of the coefficients.**Logistic Regression**– Also used in statistics, logistic regression is used in machine learning for binary classification problems. Like in linear regression, the goal is to establish the relationship between two variables by finding the coefficient values. However, unlike linear regression, a non-linear function known as a logistic function is used in the case of problems with two class values.**Linear Discriminant Analysis**– When there are more than two class values, logistic regression cannot be applicable. Linear discriminant analysis, or LDA, is used in this case. Here, predictions are made by determining a discriminant value for each class value and calculating the most significant value for the class.**CART**– Classification and Regression Trees or CART is a form of Decision Tree. Here, the decision tree represents a binary tree. Each tree node represents a single input variable (x) and the splitting point on the variable. At the same time, the leaf nodes represent the output variable (y), which helps make the prediction. These are excellent in making predictions as each split in the tree leads to more probable solutions.**SVM**– A type of classification supervised learning; support vector machines are used to find unique solutions to problems. The Support Vector Machines algorithm determines the coefficients that best define the separation of the classes by the hyperplane.**Naïve Bayes**– Another simple yet powerful classification supervised learning model, Naïve Bayes, is based on the Bayes Theorem. It is named naïve because it assumes that each input variable is free from the rest of the data, which is an unrealistic assumption.**KNN**– KNN or K-nearest neighbors is an algorithm for making predictions using the entire training dataset. It takes up a lot of space for data storage and is a lengthy process. However, the results produced by KNN algorithms are accurate and relevant to the problem.**LVQ**– This type of algorithm can be used when using KNN algorithms is not an option, as it requires hanging on to the entire training dataset. LVQ, or Learning Vector Quantization, is a form of neural network that allows data scientists to select the number of training instances they want to use to find a suitable dataset.**Bagging and Random Forest**– This is called bootstrap aggregation and is a powerful supervised learning predictive modeling. Bagging and random forest algorithms use multiple training data samples and combine them to derive better results.**Boosting with Adaboost**– Adaboost, or adaptive boosting, is a boosting algorithm best suited for binary classification. This algorithm helps create accurate predictions using a correction model that differentiates a robust classifier from a set of weak classifiers.

**Conclusion**

Organizations use the machine learning algorithms mentioned above in various combinations to derive the most accurate predictions for solving their business problems. Repeated testing using suitable ML algorithms helps businesses reach the desired results. The expert advisors and consultants of our **Machine Learning Consulting Services** assist organizations in selecting the appropriate machine learning algorithms for their business. By doing so, companies can leverage their ML techniques to attain and sustain business growth in the long run.