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Most Popular Machine Learning Models in 2023

11 mins
Updated by Ryan Glenn

Machines today can learn in highly advanced ways. Computers churn through billions of data points to rapidly detect complex patterns and solve real-world problems. How? By using machine learning models.

Machine learning is a branch of computer science that achieves one of the primary objectives of artificial intelligence (AI). This is to design systems capable of thinking for themselves while simulating and exceeding human intelligence and learning. This guide delves into the most common machine learning models used to train computers and AI systems.

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What is a machine learning model?

What is a machine learning model?

A machine learning model is a computer program that finds patterns in training data. These patterns are used to make predictions about new data. 

To make a model functional and accurate, data scientists feed it with large datasets during training. An algorithm analyzes the datasets to find certain patterns or outputs, depending on the objective. Once the process is over, the output of the algorithm is optimized into a computer program. This is essentially what a machine learning model is. 

The model then uses the patterns found in the training datasets to define specific rules and data structures. Then, it uses these to analyze new data.

For instance, machine learning models can accurately recognize objects like traffic lights or pedestrians. Say you want to develop an app to analyze a user’s facial expressions to recognize their emotions. To execute the idea, you can train a machine learning model by feeding it pictures of faces with different emotions labeled on them. Once ready, the app can deploy the model to determine a user’s mood or feelings.

Similarly, natural language processing can train a model to parse and recognize the intent behind any sentence. 

Machine learning models in a nutshell:

  • Machine learning models are trained over sets of data.
  • The model is provided with an algorithm to reason over the data available.
  • Using the algorithm, the model extracts certain patterns within the datasets.
  • Once the training is over, the model uses the “knowledge” it gained during training on previously unseen datasets to make predictions.

Note that a machine-learning model is not the same thing as a machine-learning algorithm. While that may sound obvious, both these terms are often used interchangeably, creating confusion among many.

Difference between machine learning models and algorithms

A machine learning algorithm is a mathematical method to find patterns in a set of data. Such algorithms generally draw from calculus, statistics, and linear algebra. Some common examples of machine learning algorithms are:

  • Linear Regression
  • Logistic Regression
  • Decision Tree
  • k-Nearest Neighbors (k-NN)
  • K-means

Think of a machine learning algorithm as any other algorithm in computer science. 

A machine learning model, on the other hand, is the output of an algorithm after it runs through training datasets. Or in other words, a model represents what was learned by the underlying algorithm. It generally consists of model data and a procedure to find patterns within new data and make predictions.

In short, machine learning algorithms facilitate a kind of automatic programming, whereas machine learning models represent the program.

3 types of machine learning

Types of machine learning model

Based on the method used, machine learning algorithms can be further classified into three sub-categories: 

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

Supervised learning

Supervised learning requires some degree of human oversight and assistance. The process depends on a known set of input and output data. The model learns to identify patterns that connect the input and output data. It then replicates these models to predict outcomes from new datasets.

Supervised learning comes in handy for use cases such as:

  • Inventory optimization
  • Identifying risk factors for diseases
  • Evaluating loan applications to determine an applicant’s risk factor
  • Detecting fraudulent transactions
  • Predicting real estate prices

Unsupervised learning

Unlike supervised learning, unsupervised machine learning requires no human oversight. The models are trained on raw and unlabelled data. Instead, the algorithm learns to segregate data into different classes, so each class contains a part of the data with common features.

Unsupervised learning comes in handy when identifying patterns in raw datasets or clustering similar data into groups. Common use cases include:

  • Classifying customer profiles as per their purchase or consumption behavior.
  • Classifying inventory items according to manufacturing and/or sales metrics.
  • Highlighting associations in customer data (for example, customers who bought specific types of clothing might be interested in specific types of shoes).

Reinforcement learning

Reinforcement learning is somewhat similar to supervised learning — both methods depend on the models receiving feedback. However, the model does not receive feedback for every state or input in reinforcement learning. This approach puts the model in a game-like situation. It deploys a trial-and-error method to find the desired outcome. 

The model gets either rewards or penalties depending on its actions. Its goal is to maximize the total reward. Over time, the model becomes increasingly proficient in making a series of decisions sequentially, even in uncertain and complex environments. 

Reinforcement learning’s real-life applications include, but are not limited to:

  • Training autonomous vehicles to drive and park themselves without requiring human intervention.
  • Operating traffic lights dynamically to help control traffic.

Top machine learning models in 2022

Different machine learning models deploy different types of algorithms and learning methods. Therefore, the models can be categorized into the type of learning they use. 

Supervised machine learning models

supervised learning


Classification is a predictive modeling task. It involves predicting the type or class of an object within a finite number of options (for a sample of input data).

Classification involves an extensive dataset with instances of inputs and outputs that the underlying algorithm learns from. The model uses the training dataset to find optimal ways to map input data to specific class labels.

There are two types of classification in machine learning — binary and multi-class. The binary classifier is suitable for problems with only two possible classes. For example, yes/no, on/off. Multi-class classifiers are best suited for problems with more than two possible classes.

Objective: Predict a binary outcome. 

Use cases: Classification is best used for tasks such as language identification (e.g., Google Translate), fraud detection, spam filtering, sentiment analysis, handwritten character recognition, and document search.

Examples of commonly used algorithms in classification models:

  • Logistics regression: A linear model that comes in handy for classifying binary data.
  • Decision Tree: It is based on the “if/else” principle and offers greater resistance to outliers. 
  • K-Nearest Neighbors (KNN): A simple but time-intensive model wherein the KNN algorithm looks for similarities to classify new data points.
  • Naive Bayes: Built on the Bayesian statistical model.
  • Support vector: Often used to classify both binary and multiclass datasets.


Regression models are those where the underlying algorithm takes a statistical approach to model the connection between independent variables and a dependent variable (target). It is often used for predictive modeling in which an algorithm predicts continuous outcomes.

It falls under supervised learning, where the algorithm learns with input features and output labels. The objective is to establish a relationship among the variables by predicting how changes in one variable affect the other. You could call it a “best guess” approach to come up with forecasts from various datasets. 

Objective: To predict a numeric value.

Use cases: Predicting cryptocurrency or stock prices, predicting annual revenue growth, etc.

Examples of common regression algorithms in use today:

  • Linear regression: The most basic regression model. Linear regression is best suited in cases wherein the data is linearly separable with little to no multicollinearity.
  • Ridge Regression: Linear regression with L1 regularization. Best for estimating the coefficients of multiple regression models in a situation where independent variables are highly correlated.
  • Lasso Regressions: Linear regression with L2 regularization. Lasso stands for Least Absolute Shrinkage and Selection Operator. It deploys a methodology that performs both variable selection and regularization. The goal is to enhance prediction accuracy and interpretability.
  • Support Vector Regression (SVR): Is built on a supervised learning algorithm that can predict discrete values.

Unsupervised machine learning models

machine learning models unsupervised learning

Artificial neural networks (ANNs) 

Depending on their use cases, ANNs can belong to either the supervised or unsupervised learning category. In supervised learning, an ANN is under the supervision of an educator (for example, a data scientist or system designer). The educator utilizes their knowledge of the system to help the network prepare with labeled data sets.

In unsupervised learning, an ANN is most helpful when augmenting the training data sets with class IDs becomes difficult or impossible. Such situations usually arise when we don’t know the system.

Artificial neural networks, also known as neural networks, are roughly modeled on the human brain. They are capable of using “machine perceptions” to understand sensory inputs. Each artificial neuron connects with many other neurons to create a web-like network. The millions of neurons in this network collectively give rise to a cognitive structure.

Any real-world data, such as music, pictures, text, etc., require translation into patterns the algorithm recognizes. These patterns are usually expressed as numerical and encoded in vectors. Once the training is over, a neural network can cluster and process massive volumes of data that would require humans decades to extract any value from. 

An easily recognizable application of a neural network is Google’s search algorithm.

Objective: Clustering, classification, pattern recognition.

Use cases: facial recognition using ANN, data-intensive applications, autonomous vehicles, search engines, etc.

Examples of machine learning models using ANN:

  • Multi-Layer Perceptron: A multilayer perceptron (MLP) is a class of a feedforward AMM. These are the simplest type of deep neural networks and consist of a series of connected layers. MLP machine learning models can be a good fit for resource-intensive deep learning architectures.
  • Convolution Neural Networks: A Convolutional Neural Network (ConvNet or CNN) is an ideal machine learning vision that makes “computer vision” possible. Feed it a series of visual data, and the CNN algorithm automatically extracts the desired input to complete a task (e.g., facial recognition).
  • Recurrent Neural Networks (RNN): recurrent neural network (RNN) uses sequential data feeding to address time-series problems of sequential input data. RNN models are most commonly used in natural language processing because they are capable of processing data with a variable input length.


Clustering is a methodology in machine learning where the model is trained to group similar objects together. In other words, it groups unlabelled datasets.

It does so by finding similar patterns in an unlabelled dataset, such as color, size, shape, behavior, etc. The algorithm then segregates them as per the presence and absence of the patterns. Each group or cluster receives a cluster ID for easy identification. The machine learning model uses these IDs to simplify and process complex datasets.

Apart from statistical data analysis, the clustering technique also comes in handy with consumer segmentation and data tagging tasks. 

Objective: To group similar objects or data points together.

Use case: Market segmentation, social network analysis, anomaly detection, statistical data analysis, image segmentation, etc. To name an easy-to-relate example, platforms like Amazon and Netflix rely on the clustering technique to bring you all product and content recommendations on their apps.

Examples of algorithms in clustering-based machine learning models:

  • K-Means: A model powered by the K-Means algorithm. It segregates the dataset by grouping the samples into clusters of equal variance. It is possibly among the simplest clustering models but might develop a few drawbacks due to high variance.
  • K-Means++: This model deploys a modified variant of the K-Means algorithm. It relies on a smart centroid initialization technique. The rest of the algorithm is similar to K-Means. 
  • Agglomerative clustering: In this model, the underlying algorithm treats each data point as a single cluster before merging them gradually. Its bottom-up cluster hierarchy can be represented as a tree structure.
  • DBSCAN: A model powered by the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The algorithm segregates the high-density areas in the data points by areas of low density.

Dimensionality reduction

In dimensionality reduction models, the underlying algorithms reduce the number of random variables under consideration. This is done by obtaining a set of principal variables. 

“Dimensionality” refers to the number of predictor variables a machine learning model uses to predict an independent variable (target). 

More often than not, the number of variables is too high in the average “real-world” dataset. This creates the issue of overfitting. 

In most cases, not all variables contribute equally toward achieving the desired output. In fact, in the majority of cases, it makes perfect sense to preserve variances with fewer variables. Most dimensionality reduction techniques fall under the category of either feature elimination or feature extraction.

Objective: Generalizing data and distilling the relevant information.

Use cases: Anomaly detection, recommender systems, modeling semantics, document search, topic modeling, and so on.

Examples of algorithms used in dimensionality reduction machine learning models:

  • Principal Component Analysis (PCA): PCA is among the most common algorithms used in Dimensionality Reduction models. It projects higher dimensional data (say, three dimensions) to a smaller space (for example, two dimensions), resulting in dimensionality reduction. In other words, PCA creates fewer new variables out of a larger number of predictors. It does so in a way that the new variables are independent of each other but somewhat less interpretable.
  • t-SNE: Stands for t-Distributed Stochastic Neighbor Embedding. In this context, the word “Stochastic” refers to a not-definite but random probability. The word “Neighbor” refers to being concerned about retaining the variance of only the neighboring points. “Embedding” is just plotting the data into lower dimensions. So, t-SNE is an algorithm that produces slightly varying results each time on the same data set. The ultimate objective is to retain the structure of neighboring points. t-SNE-based models are best suited for embedding higher dimensional data for data visualization.
  • Singular Value Decomposition (SVD): SVD is one of the most popular techniques for dimensionality reduction when data is sparse. By “sparse data,” we mean instances with rows of data where many of the values are 0 (zero). This is common in ML applications such as recommender systems — for example, when a user rates only a few movies or songs in the database.

Reinforcement machine learning models

In addition to the ones described above, there are also several machine learning models powered by algorithms such as:

  • Q-Learning
  • State–Action–Reward–State–Action (SARSA)
  • Deep Q-network  (DQN)
  • Asynchronous Advantage Actor Critic (A3C)

These models are mostly used for executing complex tasks without any training data. Popular use cases include guiding robotic motion, enhancement of treatment policies in healthcare, autonomous transport, trade execution in finance, text mining, and so on.

Which machine learning model is the best?

There is no such thing as a singular best machine learning model. Different models come in handy in different use cases. In fact, many complicated systems, such as autonomous vehicles or sophisticated military hardware, may require multiple models to function coherently in synchronization. According to fortune insights, machine learning is a growing industry: it’s expected to reach a value of $209.91 billion by 2029. These models will only become more important and widely deployed in the years to come.

Frequently asked questions

What are machine learning models?

What are the main 3 types of machine learning?

What is a machine learning algorithm?


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