With the increase in technological innovations, and improved processes and codes, ML algorithms have become more capable to learn from data available and self-improve over time (i.e., human intervention is no longer needed). The learning part talked about is basically the learning function, that's obligated to map the input and output, and learn the hidden structure of unlabeled data.

With regards to the power of machine learning and its increasing usage, in this article, we will discuss the most commonly used and popular machine learning algorithms. Before that, let's commence with the three main categories that every machine learning algorithm comes under.

The Main Categories of Machine Learning Algorithms

The various commonly used and popular ML algorithms that we'll discuss in today's article are segregated into the following main heads of ML algorithms:

●       Supervised Learning: Over a set of observations, a supervised learning algorithm models the relationship between a feature (independent variable) and a label (target). The model is then used to forecast the labeling of new observations with features. It can be a classification task (discrete response) or a regression task, depending on the nature of the response (continuous response).

●       Unsupervised learning: These algorithms attempt to discover unlabeled data's structure.

●       Reinforcement learning: It operates based on behavior and reward. Agents learn to achieve their objectives by calculating rewards for actions in an iterative manner.

A List of Most Commonly Utilized ML Algorithms

So, here are the most popularly used ML algorithms for you to understand, and gain knowledge of:

1. Linear Regression

Linear regression is where we try and develop a relationship between the dependent and independent factor(s) or variable(s), and we do this by fitting a best line. We leverage it to evaluate real or actual values based on the constant variables or factors, for instance, the cost of a house, the total revenue done, the number of calls dialed or received, etc. The best fit line that was just mentioned is popularly known as the regression line and the linear equation for the same is Y= a *X + b.

2. Logistic Regression

Beginners having less knowledge confuse it to be a regression algorithm, because of the name. However, in reality, it's a classification algorithm. We leverage such an algorithm to ascertain discrete values, such as "0/1", "Yes/No", "True/False", etc. Logistic regression is also termed logit regression, and since its job is to predict the probability the output will always lie between 0 and 1.

3. Decision Tree

Another commonly used and very popular machine learning algorithm, the decision tree is a sort of supervised learning algorithm that professionals leverage for classification problems. Leveraging the decision tree algorithm we first split the population (dataset) into two or more homogeneous sets, and it's done dependent on the most effective or meaningful attributes to create as distinct groups as possible. This algorithm will work fine for both continuous and categorical dependent variables too.

4. SVM (Support Vector Machine)

Support vector machine comes under the classification method. In SVM, every data is depicted or displayed as a point in n-dimensional space (n - number of features), with the value of each feature becoming the value of a specific coordinate. If we only had two features of an individual like the weight and hand length, you can plot these variables in two-dimensional space, with every point having 2 coordinates, and these coordinates are termed as Support Vectors.

5. Naive Bayes

Naive Bayes theorem is a highly popular algorithm based on Bayes' theorem and the predictor independence assumption. In simple terms, a Naive Bayes classifier assumes that the presence of one characteristic in a group is independent of the presence of every other characteristic. A fruit is called an orange if it is tangerine or orange in color, round, and 2.5 to 3 inches in diameter. Even though these characteristics are mutually dependent or reliant on the presence of all other characteristics, a naive Bayes classifier might deem each of these qualities to contribute individually to the plausibility that this fruit is an orange.

6. k- Nearest Neighbors (kNN)

The k-nearest neighbor (kNN) algorithm is a non - parametric supervised learning classification algorithm that utilizes proximity to classify or anticipate the grouping of a data point. It can be applied to regression or classification tasks, but it is most commonly seen as a classification model that assumes commonalities are nearer together.

7. K-Means

Another example of an unsupervised algorithm type that aims to solve the clustering problem is K-Means. Its procedure follows a straightforward approach to categorizing a given set of data using only predefined clusters (k clusters). Data within a cluster can both be homogeneous and heterogeneous to peer groups.

8. Random Forest

Random Forest is a popular term for a set of decision trees. The term "Forest" is leveraged because it's a collection of decision trees. Here, each tree allocates a classification and votes for a particular class, and this is done to ensure it can define a new object dependent on attributes. The classification that receives the most votes is chosen by the forest (over other trees).

Final Words

With this, we end today's article on the most commonly used machine learning algorithms. To reiterate the various algorithms that we discussed, they are Linear Regression, Logistic Regression, Decision Tree, Support Vector Machine, Naive Bayes, kNN, K-Means, and Random Forest.

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