What is Machine Learning?

Although artificial intelligence (AI) and machine learning (ML) are often used interchangeably, they are not the same.

artificial intelligence is a field of computer science focused on creating systems and machines capable of reasoning, learning, and acting in ways that resemble human intelligence. It also encompasses technologies designed to handle data volumes far beyond human analytical capacity. AI is a multidisciplinary domain that draws from data analytics, statistics, software and hardware engineering, neuroscience, and even philosophy.

Machine learning, by contrast, is a specific application of AI that involves training systems to perform tasks without explicit programming. It is primarily used to extract insights from data through methods such as neural networks, supervised and unsupervised learning, decision trees, and linear regression.

Just as machine learning is a subset of AI, deep learning is a subset of machine learning. Deep learning trains neural networks using data, where each network is composed of artificial neurons—computational nodes that classify and analyze information. Data passes through the first layer of the network, with each node making decisions and transmitting them to nodes in subsequent layers. When models include more than three layers, they are referred to as “deep neural networks” or “deep learning.” In modern applications, some neural networks contain hundreds or even thousands of layers.

Machine learning operates by training algorithms on datasets to achieve specific outcomes, such as detecting patterns or recognizing objects. It is essentially the process of refining a model so it can accurately predict results based on the training data provided.

When the training data is of high quality, the accuracy of the model improves as it is exposed to more samples. During training, the algorithms fits the model to the data through what is known as the “fitting process.” This involves applying a loss function to measure errors and using an optimization method, such as gradient descent, to adjust model parameters and reduce those errors.

If the predicted outcome does not align with the expected result, the algorithms is retrained repeatedly until it produces accurate responses. In essence, the algorithms learns from the data and generates outcomes by identifying whether the relationship between input and output follows a line, cluster, or other statistical correlation.

When discussing the different types of machine learning, we are essentially referring to the models used for training. Broadly, there are four primary categories of machine learning models.

Supervised learning is one of the most common approaches. It relies on labeled training data (structured data) to map features to specific outcomes. In this method, the output is already known—for example, recognizing an image of an apple. The model is trained using datasets where the input is paired with the correct output. Simply put, to train the algorithms to identify apples, you provide it with images labeled as apples.

Some widely used supervised learning algorithms include:

Unsupervised learning is a machine learning approach that works with unlabeled or unstructured data to identify patterns and relationships. Unlike supervised learning, the expected outcome is not predefined. Instead, the algorithm learns directly from the data without human guidance and organizes it into groups based on shared characteristics.

For instance, if the algorithm is given a mix of images of apples and bananas, it will independently determine which images belong to each category. This makes unsupervised learning highly effective for descriptive modeling and pattern recognition.

Commonly used unsupervised learning algorithms include:

Semi-supervised learning is a hybrid approach that uses a combination of labeled and unlabeled data. In this method, only part of the dataset is labeled, and the algorithm must learn how to organize and interpret the remaining data to achieve the correct outcome. For example, a model may be informed that the result should identify a pear, but only a portion of the training data is labeled as pears. The algorithm uses this partial guidance to make sense of the rest of the dataset.

Reinforcement learning, on the other hand, can be described as a “learn by doing” process that relies on trial-and-error experimentation. In this approach, an agent is trained to complete a specific task through a continuous feedback loop. The agent receives positive reinforcement for desirable performance and negative reinforcement for mistakes, gradually improving its ability to achieve the goal.

A notable example of reinforcement learning is when Google researchers trained an algorithm to play the board game Go. Without prior knowledge of the game’s rules, the model began by making random moves and progressively learned effective strategies through feedback. Over time, it achieved such a high level of skill that it was able to defeat a professional human player.

Pattern Recognition: The more data a machine learning algorithm processes, the more effective it becomes at identifying trends and patterns. For example, an e-commerce platform may use machine learning to analyze customer shopping behaviors, enabling it to deliver better product recommendations or uncover insights that lead to new business opportunities.

Automation: Machine learning and artificial intelligence can automate repetitive and time-consuming tasks, allowing employees to focus on higher-value work. Tools such as robotic process automation streamline routine business operations, while computer vision and object detection algorithms enable robots to pick and pack items on assembly lines. In addition, always-on machine learning models for fraud detection and threat assessment can identify potential security risks before they escalate.

Continuous Improvement: With the right data, machine learning algorithms continuously evolve to become faster and more accurate. This improvement occurs through retraining with new datasets and incorporating real-world feedback from users, ensuring the models remain effective and relevant over time.

Bias Potential: Machine learning models are only as reliable as the data they are trained on. If the input data contains bias, the resulting predictions and outcomes will also reflect that bias, potentially leading to inaccurate or unfair results.

Data Acquisition: Many machine learning applications, especially those based on supervised learning, require large volumes of high-quality data. Collecting, cleaning, and preparing structured data can be challenging—particularly when information is scattered across siloed systems within an organization.

Technical Expertise Required: Although cloud platforms and AI vendors continue to simplify machine learning implementation, organizations still need skilled programmers and data scientists to design models, interpret results, and ensure accuracy. Technical expertise remains a critical factor for success.

Resource Intensive: Building and training machine learning models can be both time-consuming and resource-heavy. Significant computing power and substantial employee effort are often required to process large datasets and generate meaningful results.

Machine learning is applied across a wide range of industries and use cases. Some of the most common include: