The machine executes multiple AI algorithms to complete the jobs. Algorithms are machine learning subsets that automate the machine learning process. In addition, these algorithms instruct the computer on how to acquire knowledge independently.

The ml algorithms are self-modifying and automated, allowing them to improve over time. They are classified into four types:

• Supervised
• Unsupervised Learning
• Semi-supervised Learning
• Reinforcement Learning

However, these four types of ml algorithms are further subdivided.

Linear regression

Linear regression is a supervised machine learning method used by the Train Using AutoML tool to identify a linear equation that best explains the relationship between the explanatory variables and the dependent variable. It is accomplished by utilising least squares to fit a line to the data.

It is used to assess the nature and degree of the relationship between a dependent variable and a set of other independent variables. It aids in developing models for making predictions, such as projecting a company's stock price.

Logistic regression

Logistic Regression is a fundamental and widely used classification algorithm. It is known as 'Logistic Regression' since the basic technique is identical to Linear Regression. The name "Logistic" is derived from the Logit function employed in this categorization method.

It is a popular method for solving prediction and classification issues. Among these use cases are Fraud detection: Logistic regression methods can assist teams in identifying data anomalies that predict fraud.

Decision tree

A decision tree is a non-parametric supervised learning approach that we can use for classification and regression applications. It has a hierarchical tree structure consisting of a root node, branches, internal nodes, and leaf nodes.

They are precious for data analytics and machine learning because they divide large amounts of data into smaller, more digestible chunks. These domains are frequently utilised for prediction analysis, data classification, and regression.

SVM algorithm

SVM is a supervised machine-learning technique that may be used for classification and regression. Though we call them regression problems, they are best suited for categorization. The SVM algorithm aims to find a hyperplane in an N-dimensional space that classifies the input points.

Handwriting recognition, intrusion detection, face identification, email classification, gene classification, and web page generation all use SVMs. SVMs are used in machine learning for this reason. It can handle classification and regression on both linear and non-linear data.

Naive Bayes algorithm

The Naive Bayes classifier is a probabilistic classifier founded on probability models with high independence assumptions. However, the independence assumptions frequently do not affect reality. As a result, they are regarded as naive.

We can use it to solve multi-class prediction issues. If the premise of feature independence remains true, it can outperform other models using far less training data. Naive Bayes works well with category input variables rather than numerical values.

KNN algorithm

The k-nearest neighbours algorithm is a non-parametric, supervised learning classifier that employs proximity to create classifications or predictions about an individual data point's grouping.

Because it delivers exact predictions, the KNN algorithm can compete with the most accurate models. As a result, the KNN algorithm can be used for applications requiring high accuracy but not a human-readable model. The distance measure determines the accuracy of the predictions.

K-means

The k-nearest neighbours algorithm is a non-parametric, supervised learning classifier that employs proximity to create classifications or predictions about an individual data point's grouping.

Because it delivers exact predictions, the KNN algorithm can compete with the most accurate models. As a result, the KNN algorithm can be used for applications requiring high accuracy but not a human-readable model. The distance measure determines the accuracy of the predictions.

Random forest algorithm

A random forest algorithm is a supervised machine learning algorithm widely used in Machine Learning for classification and regression issues. We know that a forest is made up of many trees, and the more trees there are, the more robust the forest is.

Data scientists use random forests on the job in various industries, including finance, stock trading, medical, and e-commerce. It's utilised to forecast factors like customer behaviour, patient history, and safety, which help these businesses run smoothly.

Dimensionality reduction algorithms

Dimensionality reduction strategies are used to reduce the number of input variables in training data. When dealing with high-dimensional data, it is frequently advantageous to reduce the dimensionality by projecting the data to a lower-dimensional subspace that captures the "essence" of the data.

Gradient boosting algorithm and AdaBoosting algorithm

AdaBoost is the first boosting algorithm with a specific loss function. Gradient Boosting, on the other hand, is a generic method that aids in the search for approximate solutions to the additive modelling problem. Gradient Boosting is thus more adaptable than AdaBoost.

Furthermore, the gradient boosting approach employs the gradient descent method to identify the optimal point by constantly minimising the loss function. The Gradient Boosting approach theoretically outperforms AdaBoost.