What are Machine Learning Algorithms?

The idea of a machine learning algorithm is identical to that of any other algorithm in computer science. A machine learning algorithm is a data-driven method for developing a production-ready machine learning model. If you think of machine learning as a train for completing a task, then machine learning models are the engines. The optimal machine learning method is decided by the business problem at hand, the dataset’s complexity, and the available resources. In this article, we will be discussing the most significant machine learning algorithms used in the modern day world.

The Most Effective Machine Learning Algorithms

Logistic Regression

This algorithm employs a logistic function to forecast the outcome of a categorical dependent variable using predictor variables. The probabilities or possibilities associated with the result of a particular experiment are expressed as a function of explanatory variables. Logistic regression methods help predict the likelihood of falling into a certain level of the categorical dependent variable based on the given predictor variables.

In epidemiology, the logistic regression method determines disease risk factors and develops suitable preventive measures. They are used to predict a political election or determine if a person will vote for a specific candidate. It is used to categorize a set of words as nouns, pronouns, verbs, and adjectives. Moreover, it is used in weather forecasting to predict the probability of rain.

Credit rating systems also use the algorithm to predict an account’s default for risk management reasons.

Naive Bayes Algorithm

The Naive Bayes Classifier is one of the most frequently used machine learning methods for classifying objects based on their similarities. It is based on the well-known Bayes Theorem of Probability and is used to build machine learning models for illness prediction and classification of documents. It is a precise classification of words for subjective content analysis based on the Bayes Probability Theorem. The Bayes theorem is used to compute probabilities in this classification method. The basic assumption of Naive Bayesian algorithms is that all attributes are considered to be independent. It is a fundamental technique that is easy to apply. It is particularly beneficial for large datasets and is compatible with text datasets.

Facebook uses sentiment analysis to evaluate whether a status update conveys positive or negative emotions. Additionally, the Naive Bayes algorithm is used to classify news articles in the categories of Technology, Entertainment, Sports, and Politics.

Artificial Neural Network Algorithm

It is a subfield of artificial intelligence that is concerned with brain simulations. It is a kind of neural network composed of connected neurons. This related structure can provide many predictions for regression and classification problems. A neural network comprises many layers: an input layer, a hidden layer, and output layers. There may be many hidden layers. The neural network’s computations take place in the hidden layer. The actual weights and biases formulas and the application of activation functions are provided here. These activation functions are essential for organizing and reducing the output and often tackle nonlinear issues like handwriting recognition. Because they need complex mathematical calculations, ANNs are computationally costly.

Financial organizations use machine learning algorithms to optimize loan application assessment, bond rating, marketing targeting, and credit score. Additionally, they can identify instances of credit card theft.

Moreover, Buzzfeed organizes and searches videos and images using image recognition methods from artificial neural networks. Google also uses Artificial Neural Networks to recognize a voice, images, and other patterns (such as handwriting recognition). Google uses ANNs to identify spam and several other tasks.

Artificial Neural Networks are excellent for temperature control, equipment operation, and problem diagnosis in robotic manufacturing.

Decision Tree

A decision network diagram is a visual representation that makes use of branching to show all possible outcomes of a choice under defined conditions. A decision tree’s internal node reflects an attribute test. Each branch of the tree indicates the development of the trial. At the same time, the leaf node represents a particular class label, i.e., the choice made after all of the characteristics have been calculated. The path from the root to the leaf node serves as an illustration for the classification criteria.

Classification trees are the most often used decision trees for categorizing a dataset according to the response variable. They are often used when the response variable is categorical.

Regression trees are used When the response or target variable is constant or numerical. They are often used in prediction issues, in contrast to categorization.

Additionally, decision trees are classified into two categories based on the nature of the target variable: Continuous Variable Decision Trees and Binary Variable Decision Trees. The target variable is used to identify the kind of decision tree required to solve a particular problem.

Decision trees are a typical kind of machine learning technique that is very helpful for option pricing in finance. Remote sensing is an area of application for pattern recognition using decision trees. Moreover, Banks use decision tree algorithms to classify loan applicants based on their tendency for payment failure.

Linear Regression

The Linear Regression technique is used to visualize the connection between two variables and the impact of one variable’s change on the other. The algorithm demonstrates how changing the independent variable affects the dependent variable. Independent variables are called explanatory variables since they provide insight into the factors affecting the dependent variable. The dependent variable is often referred to as the interest component or predictor. Linear regression is a method for estimating continuous values that are used to calculate actual values. The most frequently used applications of linear regression include predicting property prices, sales, weather, and assessing employee compensation. The primary goal of linear regression is to identify the line that fits the predictions the best. Y=a*x+b represents the dependent variable, and x denotes the collection of independent variables. The slope is indicated by the letter a, while the intercept is characterized by the letter b.

Linear regression has been used in business to predict sales based on historical trends. If a company regularly grows its sales month after month, it may forecast future sales using a linear regression analysis of the monthly sales data.

Linear regression is advantageous in the insurance and finance sectors for risk assessment. A health insurance company may use linear regression to ascertain the number of claims per customer based on their age. This study aids insurance companies in identifying that older customers are more likely to make insurance claims. These analytical results are essential for making critical business decisions and for risk management.

Conclusion

We have covered different kinds of machine learning and their related algorithms in this post. Machine learning is crucial because it enables companies to get insight into customer behaviour and operational patterns and assist in developing new goods. Several large companies, like Facebook, Google, and Uber, have integrated machine learning into their operations. Machine learning has become a major competitive differentiator for many companies.

Algorithms use patterns, and even the most seasoned data scientist cannot anticipate which algorithm will work best without first experimenting with various methods. While there are many more Machine Learning algorithms, the ones included in this article are the most often used.