Organizations worldwide are significantly investing in Artificial Intelligence and Machine Learning technologies to drive innovation, optimize operations, and gain a competitive edge by leveraging data for more informed decision-making and automation of complex tasks.
The global machine learning market is expected to reach USD 225.91 billion by 2030, primarily due to the rise in automation adoption, the availability of large datasets, and increased investments in research and development activities (Source: Fortune Business Insights).
Machine learning models rely on data and algorithms. They can often be novel and overwhelming tasks for several companies. Understanding the different machine learning models and their use cases is important for businesses that are aiming to effectively embrace AI and its powerful capabilities.
This article explores the definition of machine learning models and their various types. It provides examples of how enterprises can leverage machine learning in their projects and highlights the potential impact these models can have on those looking to explore their applications.
What is a Machine Learning Model?
A machine learning model is a program that helps find patterns or make decisions from previously unseen datasets. Trained on datasets and an algorithm, the model can be used to predict outcomes or make effective decisions. Machine learning models improve their accuracy over time as they are exposed to more data.
Image recognition is one of the machine learning model examples. You can use the convolutional neural network (CNN) algorithm trained on images to find or predict the answer. Most applications can use a pre-trained or foundational model, such as Meta AI’s Segment Anything model.
The following three key components make up a machine learning model:
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Training: Training comprises learning new information. With different models being employed for various use cases, It is important to figure out how each will learn the information from those models.
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Data: How the machine learns data is an important part of training. This data serves as the input for the machine learning model.
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Algorithm: People often confuse models with algorithms, which may often be used interchangeably. Data is trained on algorithms first before it becomes a model.
It is often difficult to analyze a large dataset and accurately predict the outcome. Companies deal with terabytes of data to make such decisions, which requires a more powerful and reliable solution to simplify the process.
Machine learning data models strive to find patterns in the given data, evaluate it, and then train on it. Based on learning, the model recognizes patterns and makes predictions accordingly. Undoubtedly, machine learning models are a game-changer for innovators and industries alike. From finance and healthcare to retail and hospitality, these models are transforming how businesses run.
Types of Machine Learning Models
Machine learning models can be grouped according to the training data and tasks. Each type of model consists of an algorithm, training data, and a learning mechanism that allows it to improve predictions or decisions over time.
There are five major machine learning models:
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Supervised Learning: The algorithm is trained on labeled data to develop and optimize machine learning models. This model needs to map the input to the output. This learning is mostly used for image recognition, fraud detection, and risk assessments.
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Unsupervised Learning: This model uses unlabelled data to train machines. There is no output variable. Instead, the variable learns from the data, finds patterns and features in the data, and returns the output.
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Semi-supervised Learning: It is a combination of supervised and unsupervised learning techniques. The dataset has both labeled and unlabelled data. An example of semi-supervised learning is Generative Adversarial Networks (GANs), a deep learning technique that trains two neural networks and generates unlabeled data.
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Reinforcement Learning: Reinforcement learning trains a machine to take suitable actions and maximize reward in a particular situation. It uses an agent and an environment to produce actions and rewards.
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Self-supervised Learning: Unlike other models that require massive labeled datasets, this model is trained on unlabelled data. It transforms unstructured datasets into structured ones.
Understanding these algorithms is critical when you are trying to build an efficient machine learning model. The more you understand these algorithms—how they work, which parameters yield the best results, and how to optimize the process—the better your model will perform.
It has become essential for companies to understand artificial intelligence and machine learning technologies and commit to using or learning about them to stay competitive and participate in a rapidly growing and scalable market. Companies often face challenges with their AI projects due to the complexity of managing them or a lack of skilled personnel on their teams.
Tredence helps businesses build and deploy reliable machine learning models. Its MLOps solution enables companies to overcome challenges such as hiring specialists or learning about machine learning architecture and operations. Through its state-of-the-art platform, Tredence bridges the gap between the ops and development team, allowing you to run thousands of machine learning models at scale, all at once.
Tredence’s ML Ops has automated workflows. Its pre-built accelerators can help you track your model’s degradation and manage the workflow of your code. You can focus on creating an effective strategy while Tredence does the grunt work of reducing outages and simplifying model monitoring.
As data becomes increasingly vital, artificial intelligence and machine learning have become key components of everyday business operations. From analyzing patterns to predicting customer behavior, companies are increasingly using machine learning to strengthen their IT infrastructure and create applications that benefit people in the long run.
Popular Machine Learning Models
Machine learning models can be categorized as either supervised or unsupervised. Let's explore how other models fit into these two categories.
Two of Supervised learning has two subcategories: Regression and Classification.
Regression Model: In a regression model, we find a target value based on independent predictors. You can use this model to find the relationship between dependent and independent variables. Additionally, the output is continuous.
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Decision Tree: This type of tree is typically used for regression and classification tasks. It has a hierarchical structure consisting of nodes (root, internal, and leaf) and branches.
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Random Forests: These are ensemble learning techniques that build upon decision trees by creating multiple decision trees using bootstrap datasets or the original data, while randomly selecting a subset of variables at each step of the tree construction.
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Neural Network: This is a multi-layered model inspired by the human mind. Akin to neurons in our brain, each neural represents a node. The network consists of input layers, hidden layers, and output layers. Each node in the hidden layer represents a function where the input goes through, ultimately leading to the output.
Classification: In classification, the output is discreet. Some of the most common types of classification include:
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Logistic Regression: It is similar to linear regression but is used to model the probability of a finite number of outcomes–typically two. The output values can only be between zero and one.
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Support Vector Machine: It is a supervised classification technique that aims to find a hyperplane in n-dimensional space that can distinctly classify data points.
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Naive Bayes: A classifier based on the Bayes theorem acts as a probabilistic machine learning model for classification tasks. It can be used for spam filtering, sentiment analysis, or recommendation systems.
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Decision Tree, Random Forest, and Neural Network: These models follow the same logic as regression models, with the only difference being that the output is discrete rather than continuous.
Unlike supervised learning, unsupervised learning is used to draw inferences and find patterns from input data without referring to labeled outcomes. Clustering and dimensionality reduction are the two primary methods of unsupervised learning.
Clustering: Clustering involves the grouping of data points. It is often used for customer segmentation, fraud detection, and document classification. Common clustering techniques include:
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K-Means Clustering: K-means clustering, a simple and commonly used method, clusters your data points on a given number of clusters. You need to define the number of clusters beforehand. It then distributes the data points among the other K-clusters to minimize the sum of the distances from each point to the main cluster.
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Hierarchical Clustering: It is a clustering method that creates a hierarchical tree of objects that needs to be clustered. The tree represents the relationships between the objects and shows how objects are clustered at different levels.
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Mean Shift Clustering: It observes and groups data based on its unique characteristics. This type of unsupervised learning learns directly from the data it is fed.
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Density-based Clustering: Density-based Spatial Clustering Applications with Noise (DBSCAN) work by defining the cluster as a maximal set of density-connected points. Two parameters are considered: Epsilon (the maximum radius of the neighborhood) and minimum points (the minimum number of points in the epsilon-neighborhood to define a cluster).
While each technique has different methods of finding clusters, they all aim to achieve the same goal.
Dimensionality Reduction: It is the process of reducing dimensions of your feature set. Simply put, it is the process of reducing the number of features. Most dimensionality reduction techniques can be categorized as either feature elimination or feature extraction. Principal Component Analysis (PCA) is a popular method of dimensionality reduction.
These models can be used to achieve different objectives. Many of the models mentioned above are popular. Still, every engineer or developer needs to understand the various types of machine learning models to select the most suitable one for their specific needs.
Components of Machine Learning Model
Just as we divide our tasks into bite-sized ones, prioritizing one over the other, machine learning can also be divided into five components for learning. These components are interlinked with each other.
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Representation: Just as humans can interpret knowledge in various ways, machines can do the same. Representation refers to how certain knowledge is described for the machine to learn and understand. This makes it easier for machine learning to learn, reason, and make effective decisions based on a specific goal.
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Data Storage: It helps users store and retrieve large amounts of data. Both humans and computers can utilize data storage. They can store, retrieve, and use data for advanced learning.
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Abstraction: Abstraction helps us extract knowledge about the stored data and create foundational concepts around it. This involves applying the knowledge to both known and new models.
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Generalization: Generalizations handle new and unknown data that were identified in the data used in training the model. They help you understand the quality of your model and whether or not it is overfitting.
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Evaluation: Evaluation is the last step in the learning process. It provides feedback to the user regarding the type of knowledge it has learned and how effectively it has been applied. This feedback is then used further to improve the model and the learning process.
The components mentioned above are the basic foundation for a learning model in machine learning. These principles act as a blueprint and help you build an effective machine learning model that achieves your objectives.
How to Choose a Machine Learning Model?
Selecting the right model that meets your objective may seem daunting. You need to consider various factors, such as the dataset, the task at hand, and the nature of the model, all of which influence your choice of model.
Models can be selected based on the type of data available (such as images and videos, text, speech, and numerical data) and the type of task we need to perform (classification tasks, regression tasks, clustering tasks, etc.).
Apart from this, you must consider the advantages and disadvantages of all the models in machine learning. Comprehending these will give you a clear picture of what model and algorithm to use and when.
For example, consider the Linear Regression model. Its advantage lies in its simplicity and ease of implementation. It performs well on data that exhibits a linear relationship. However, it is not suitable for data that has non-linear relationships. It also has underfitting issues and could be sensitive to outliers.
Selecting the right machine learning model becomes easier once you know your goals and what you need your model to achieve.
Applications of Machine Learning Models
Machine learning models have undeniably changed the way businesses function. They streamline mundane tasks, automate workflows, and help businesses make predictions based on historical data for effective decision-making.
Let us look at some applications of machine learning models and how they are being used today:
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Customer Service: Chatbots that act as virtual agents can easily handle text-based queries. Several businesses have implemented chatbots on their websites, allowing them to resolve queries faster.
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Financial Transactions: In the U.S., 60 percent of credit card holders are victims of fraud (Source: Security.org). To mitigate this issue, machine learning and deep learning are widely used in fraud detection where financial institutions train machine learning models and classification algorithms to identify suspicious online transactions and flag them for further investigation.
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Cybersecurity: Reinforcement learning uses machine learning to train models to analyze and respond to cyberattacks and detect intrusions.
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Transportation: Google Maps uses machine learning algorithms to assess traffic conditions and determine the fastest route (Source: Google). Furthermore, ridesharing apps like Uber and Lyft use machine learning to match riders to the drivers (Source: Medium).
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Healthcare: Machine learning can augment and accelerate human capabilities. It is already helping doctors with pattern recognition; models are trained to classify tumors that are hard to see with the human eye.
Several companies use machine learning to improve their strategic decision-making and efficiency, and enhance their customer experiences. Machine learning models not only provide companies with a competitive edge, but also drive growth and innovation.
Empowering Your Business with Tredence’s Machine Learning (MLOps)
Machine learning transforms industries, providing limitless opportunities to build and deploy life-changing applications. Businesses must leverage AI to its full potential to manage machine learning models effectively.
Tredence, one of the leading providers of data science and AI solutions, offers MLOps services optimized to streamline your machine learning operations. These services include advisory and strategy development, observability and monitoring, and ML Integration.
By utilizing state-of-the-art tools and frameworks, Tredence enables companies to mitigate issues such as a lack of experts and unscalable or complex AI projects.
Partner with Tredence today to empower your business and manage MLOps successfully.
FAQs
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What are the major types of machine learning models?
The five key types of machine learning models are supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and self-supervised learning. Each type can be used to meet different objectives, such as making predictions, improving decision-making, or finding patterns.
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What is the difference between a machine learning algorithm and a machine learning model?
A machine learning algorithm is a set of instructions that acts as a blueprint for data processing, while a machine learning model is the output of the algorithm that has been trained on data. It is primarily used for predictions or decision-making.
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Which industries use machine learning models?
Several industries apply machine learning models in their applications. These include healthcare, retail, finance, and cybersecurity. Machine learning models help predict outcomes, automate tasks, and offer tailored customer solutions.
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Editorial Team
Tredence
