Machine learning (ML) algorithms are a class of computer algorithms that can learn from data and make predictions or decisions based on that data. ML algorithms are used in a wide range of applications, including image recognition, speech recognition, natural language processing, and recommendation systems, among others. ML algorithms are generally categorized into two main types: supervised learning and unsupervised learning.

Supervised Learning Algorithms:

Supervised learning is a type of ML algorithm that is used when the data is labeled. In other words, there is a predefined outcome or output variable that the algorithm tries to predict based on the input variables. The input and output data are given to the algorithm, and it tries to find the relationship between them.

The most common examples of supervised learning algorithms are regression and classification algorithms. Regression algorithms are used when the output variable is continuous, such as predicting the price of a house based on its features. Classification algorithms, on the other hand, are used when the output variable is categorical, such as predicting whether an email is spam or not.

Some of the popular supervised learning algorithms include linear regression, logistic regression, decision trees, support vector machines (SVM), and neural networks.

Unsupervised Learning Algorithms:

Unsupervised learning is a type of ML algorithm that is used when the data is unlabeled. In other words, there is no predefined outcome or output variable, and the algorithm tries to find patterns or structure in the data on its own.

Clustering and association rule mining are two popular types of unsupervised learning algorithms. Clustering algorithms are used to group similar data points together based on their characteristics. Association rule mining algorithms are used to find relationships between different variables in the data.

Some of the popular unsupervised learning algorithms include k-means clustering, hierarchical clustering, principal component analysis (PCA), and apriori algorithm.

Deviation of ML Algorithms:

While supervised and unsupervised learning are the two primary types of ML algorithms, there are several other deviations of ML algorithms as well.

Semi-supervised learning is a type of ML algorithm that uses a combination of labeled and unlabeled data to improve the accuracy of the model. This approach is particularly useful when it is expensive or time-consuming to label all of the data.

Reinforcement learning is a type of ML algorithm that learns by trial and error. In this approach, an agent interacts with an environment, and the algorithm learns based on the feedback it receives from the environment.

Deep learning is a type of ML algorithm that is based on artificial neural networks. It is particularly useful for complex tasks such as image and speech recognition.