What Is Machine Learning?

Machine learning is a subset of artificial intelligence that gives systems the ability to learn and optimize processes without having to be consistently programmed. Simply put, machine learning uses data, statistics and trial and error to “learn” a specific task without ever having to be specifically coded for the task. 

  

What Is Machine Learning?

Machine learning is a subfield of artificial intelligence in which systems “learn” through data, statistics and trial and error to optimize processes and innovate at quicker rates. Through machine learning, computers can apply human-like reasoning and decision-making to help solve some of the world’s toughest problems, ranging from cancer research to climate change.

Most computer programs rely on code to tell them what to execute or what information to retain. This is known as explicit knowledge, which contains anything that is easily written or recorded like textbooks, videos and manuals. With machine learning, computers gain tacit knowledge, or knowledge gained from personal experience and context. This type of knowledge is hard to transfer from one person to the next via written or verbal communication.

Facial recognition is a type of tacit knowledge. We recognize a person’s face, but it can be difficult for us to describe how or why we recognize it. We rely on our personal knowledge banks to connect the dots and immediately recognize a person based on their face. Another example is riding a bike: It’s much easier to show someone how to ride a bike than it is to explain it.

Computers no longer have to rely on billions of lines of code to carry out calculations. Machine learning gives computers the power of tacit knowledge to make connections, discover patterns and make predictions based on what they learned in the past. Machine learning’s use of tacit knowledge has made it a go-to technology for almost every industry from fintech to government.

 

How Does Machine Learning Work?

Machine learning compiles input data, which can be data gathered from training sessions or other sources, such as data set search engines, .gov websites and open data registries like that of Amazon Web Services. This data serves the same function that prior experiences do for humans, giving machine learning models historical information to work with when making future determinations.  

Algorithms then analyze this data, searching for patterns and trends that allow them to make accurate predictions. In this way, machine learning can glean insights from the past to anticipate future happenings. Typically, the larger the data set that a team can feed to machine learning software, the more accurate the predictions.    

The idea is that machine learning algorithms should be able to perform these tasks on their own, requiring minimal human intervention. This speeds up various processes as machine learning comes to automate many aspects of different industries.

 

Types of Machine Learning

Before data can be used to create an AI model, it must first be prepared. To do this, developers will handle missing values, remove outliers and normalize data. Afterward they will categorize data differently depending on the learning type that best fits their needs. There is a range of machine learning types that vary based on several factors like data size and diversity. Below are a few of the most common types of machine learning under which popular machine learning algorithms can be categorized.

Supervised Learning

Supervised learning involves mathematical models of data that contain both input and output information. Machine learning computer programs are constantly fed these models, so the programs can eventually predict outputs based on a new set of inputs.

Regression and classification are two of the more popular analyses under supervised learning. Regression analysis is used to discover and predict relationships between outcome variables and one or more independent variables. Commonly known as linear regression, this method provides training data to help systems with predicting and forecasting. Classification is used to train systems to identify an object and place it in a sub-category. For instance, email filters use machine learning to automate incoming email flows for primary, promotion and spam inboxes.

Unsupervised Learning

Unsupervised learning involves working with data that contains only inputs, and then uncovering patterns or structures within the data by organizing it into similar clusters or groups. The method learns from previous test data that hasn’t been labeled or categorized and will then group the raw data based on commonalities. Cluster analysis uses unsupervised learning to sort through giant lakes of raw data and group certain data points together. Clustering is a popular tool for data mining, and it is used in everything from genetic research to creating virtual social media communities of like-minded individuals.

Reinforcement Learning

Reinforcement learning uses an autonomous agent to make decisions through trial-and-error training. Unlike other machine learning methods, it requires no labeled data. Instead, the agent interacts with an environment and receives positive or negative feedback based on its actions. Repeating this interaction and feedback cycle thousands of times trains the agent to favor actions that lead to higher rewards. Because reinforcement learning improves decision-making over time, it is widely used in robotics, autonomous vehicles and recommendation systems.

Semi-Supervised Learning

Semi-supervised learning falls in between unsupervised and supervised learning. With this technique, programs are fed a mixture of labeled and unlabeled data that not only speeds up the machine learning process, but helps machines identify objects and learn with increased accuracy.

Typically, programmers introduce a small amount of labeled data with a large percentage of unlabeled information, and the computer will have to use the groups of structured data to cluster the rest of the information. Labeling supervised data is seen as a massive undertaking because of high costs and hundreds of hours spent.

 

Machine Learning Algorithms

An algorithm is not a machine learning or AI model itself. Rather, it provides the mathematical logic to build one. While there are many machine learning algorithms available for solving different problems, these are some of the most common types.

Neural Networks

Neural networks function similarly to the human brain, analyzing data to detect patterns and relationships. This process allows machines to learn from their mistakes and improve their performance over time. 

Linear Regression

Linear regression is used to develop accurate predictions when the outputs are continuous. It aims to represent the relationship between a dependent variable and at least one independent variable with a straight line. 

Logistic Regression

Logistic regression is used to make predictions for binary classification problems — where the outputs belong to one category or another (true/false, yes/no, etc.). Common examples include spam filtering and determining whether a patient has a specific disease.

Decision Trees

Decision trees evaluate data sets by asking a series of questions that can be organized into the branches of a tree diagram. As a result, decision trees can be used to both predict the value of an outcome and sort data into categories.  

Random Forest

Random forest solves predictive modeling and classification problems by combining the results of many decision trees. This approach increases the accuracy of a model and accounts for common problems like overfitting. 

Naive Bayes

Naive Bayes works under the assumption that features are independent, meaning one feature doesn’t affect another — each feature has an equal chance of impacting the outcome. This lets Naive Bayes break down large data sets into manageable calculations.

K-Nearest Neighbors (KNN)

K-nearest neighbors (KNN) classifies data points based on their proximity to other data points. It assumes that similar data points are close to each other, calculating the distance between a value and nearby data points and assigning it to the most prevalent category. 

K-means Clustering

K-means organizes unlabeled data into clusters by grouping nearby data points together. It accomplishes this by selecting a center point — real or imagined — for each cluster and sorting data points based on the center point they’re closest to.  

 

Machine Learning vs. Deep Learning

Deep learning is a subfield within machine learning, and it’s gaining traction for its ability to extract features from data. Deep learning uses artificial neural networks (ANNs) to extract higher-level features from raw data. ANNs, though much different from human brains, were inspired by the way humans biologically process information. The learning a computer does is considered “deep” because the networks use layering to learn from and interpret raw information.

For example, deep learning is an important asset for image processing in everything from e-commerce to medical imagery. Google is equipping its programs with deep learning to discover patterns in images to display the correct image for whatever you search. If you search for a winter jacket, Google’s machine and deep learning will team up to discover patterns in images — sizes, colors, shapes, relevant brand titles — that display relevant jackets.

Deep learning is also making significant advancements in radiology, pathology and any medical sector that relies heavily on imagery. The technology leverages tacit knowledge — from studying millions of other scans — to immediately recognize disease or injury, saving doctors and hospitals both time and money.

 

Machine Learning Examples and Applications

Before the expansion of AI, machine learning was primarily used for simple pattern recognition. But over time, it has turned into a powerful tool to help power a number of modern technologies. From medical diagnostic systems to generative models, these are some of the most common applications of machine learning. 

Generative AI

While people use machine learning and generative AI interchangeably, their relation to each other is more like that of a parent and child. In short, machine learning provides the mathematical frameworks and neural network architectures that allow generative models to digest large datasets and learn underlying patterns. By analyzing these patterns, generative AI has moved beyond simple data analysis to produce entirely new content — including text, code and high-resolution images. 

Self-Driving Cars

In the automotive industry, machine learning acts as the central intelligence that transforms raw data from onboard sensors, LiDAR and cameras into real-time driving actions, making self-driving cars possible. These systems utilize sophisticated computer vision models to identify obstacles, predict the behavior of pedestrians and interpret complex traffic signals simultaneously. By constantly learning from millions of miles of diverse driving scenarios, machine learning allows autonomous vehicles to navigate unpredictable environments and make split-second safety decisions that would be impossible to hard-code with traditional software.

Content Recommendation

Social media companies leverage machine learning to create a sense of community, as well as weed out bad actors and disinformation. Machine learning fosters the former by looking at content, posts and pages other features that an individual likes and suggesting other topics or community pages based on those likes. It’s essentially using your preferences as a way to power a social media recommendation engine. The spread of misinformation in politics has also prompted social media companies to use machine learning to quickly identify harmful patterns of false information, flag malicious bots, view reported content and delete when necessary.

Credit Lending

Machine learning is used by lending and credit card companies to manage and predict risk. These computer programs take into account a loan seeker’s past credit history, along with thousands of other data points like cell phone and rent payments, to deem the risk of the lending company. By taking other data points into account, lenders can offer loans to a much wider array of individuals who couldn’t get loans with traditional methods.

High Frequency Trading

Trading firms are using machine learning to amass a huge lake of data and determine the optimal price points to execute trades. These complex, high-frequency trading algorithms take thousands — if not millions — of financial data points into account to buy and sell shares at the right moment. 

Medical Diagnostics

Radiology and pathology departments all over the world use machine learning to analyze CT and X-ray scans and find diseases. After being fed thousands of images of diseases through a mixture of supervised, unsupervised or semi-supervised models, advanced machine learning systems can diagnose diseases at higher rates than humans. Machine learning has also been used to predict deadly viruses, like Ebola and malaria, and is used by the CDC to track instances of the flu virus every year.

Pharmaceutical Development

Machine learning-enabled AI tools are working alongside drug developers to generate drug treatments at faster rates. These machine learning tools are fed millions of data points and configure them to help researchers view which compounds are successful and which aren’t. Instead of spending millions of human hours on each trial, machine learning technologies can produce successful drug compounds in weeks or months.

Personalized Shopping

The retail industry relies on machine learning for its ability to optimize sales and gather data on individualized shopping preferences. Machine learning offers retailers and online stores the ability to make purchase suggestions based on a user’s clicks, likes and past purchases. Once customers feel like retailers understand their needs, they are more likely to stay with that company and purchase more items.

Identifying Consumer Trends 

Machine learning has also been an asset in predicting customer trends and behaviors. These machines look holistically at individual purchases to determine what types of items are selling and what items will be selling in the future. For example, maybe a new food has been deemed a “super food.” A grocery store’s systems might identify increased purchases of that product and send customers coupons or targeted advertisements for all variations of that item. Additionally, a system could look at individual purchases to send you future coupons.

 

Benefits of Machine Learning 

Improved Efficiency

Machine learning systems execute complex, repetitive workflows by learning from iterative data rather than relying on manual programming. This form of automation eliminates human error in routine tasks and can lead to higher operational efficiency, and may help organizations and workforces to pivot from tedious administrative tasks to high-value, strategic initiatives. 

Better Business Insights

While traditional data analytics focus on interpreting historical data to explain past events, machine learning enables a shift toward predictive and prescriptive intelligence. By identifying subtle correlations within massive datasets, these models allow businesses to anticipate shifting market trends and consumer behaviors in real-time, transforming raw information into a proactive roadmap for future growth.

Improved Business Scalability

Machine learning provides the essential infrastructure for rapid expansion by automating high-volume services, such as customer support or data input, without a significant increase in overhead costs. This type of flexibility can help companies avoid operational bottlenecks that limit scaling efforts. 

Enhanced Personalization

Machine learning enables businesses to hyper-personalize user experiences by analyzing individual preferences and past interactions. By delivering tailored recommendations at scale, companies can foster deeper brand loyalty and increase conversion rates, creating competitive  advantages over competitors. 

 

Machine Learning Challenges

High Development Costs

Building and deploying a machine learning or AI system can cost between tens of thousands to millions of dollars in order to build the necessary infrastructure and recruit the right talent. Because of the enormous costs associated with the technology, many businesses may be priced out from developing custom machine learning systems and opt for ready-made products built with non-proprietary data.  

Data Bottlenecks

Machine learning systems rely on tremendous amounts of data to produce accurate outputs. Training machine learning systems on low-quality or insufficient data can lead to substandard outputs, including hallucinations, eliminating their effectiveness. 

Lack of Transparency

The computational process powering machine learning models is often incomprehensible to humans. Because of this lack of transparency, it is virtually impossible to track how the system generates an outcome and to know if there are biases in the training. All of this can cause people and businesses to be wary of machine learning systems, potentially limiting their mass adoption. 

Model Performance

With high development costs and data bottlenecks already being serious challenges for machine learning, companies developing these systems must ensure their models provide a solid return on investment — overcoming common performance issues such as producing overly simplistic outputs or degrading over time due to model drift.

 

History of Machine Learning

Machine learning has evolved from early, rule-based experiments to today’s large-scale multimodal systems powering generative AI. The brief timeline below tracks the development of machine learning from its beginnings in the 1950s to its maturation during the twenty-first century.

Claude Opus 4.7 Release (April 2026)

Anthropic released Opus 4.7 making improvements to its most advanced model including better coding software engineering capabilities. However, it is not as advanced as the capabilities found with Mythos. According to the company, Opus 4.7 is also more creative when completing work tasks like generating documents and presentations than previous generations. Additionally, the model possesses cybersecurity knowledge, but, not to the same proficiency as Mythos. 

Claude Mythos Limited Release (April 2026)

Anthropic released a preview of its new Mythos model, which has advanced coding, reasoning and agentic capabilities. Additionally, the model has specialized proficiency in cybersecurity, allowing it to spot vulnerabilities across all operating systems and web browsers. Because of the risks Mythos poses in the hands of bad actors, Anthropic decided against a general release, instead granting access to select partners through Project Glasswing. 

Meta Muse Spark Release (April 2026)

Meta released Muse Spark, the first model to come from its Superintelligence Labs. Muse Spark is a foundational model with complex reasoning, multimodal and agentic capabilities. In benchmarks, the model was comparable to current ones from competitors, but showcased a big technical leap for Meta compared to its previous AI models. 

GPT-5.4 Release (March 2026)

OpenAI released GPT-5.4, an update to its GPT-5 model. The company designed the model to improve reasoning and multimodal capabilities. In testing, OpenAI also stated that the GPT-5.4 update resulted in reduced hallucination and better coding accuracy and agentic task execution. 

Gemini 3.1 Pro Release (February 2026)

Google released Gemini 3.1 Pro, an upgrade to its most advanced model that is designed to tackle complex problems and advanced reasoning tasks. The model showcased improvements in several core areas, including in scientific knowledge, agentic coding and multi-step workflows when compared to competitors and its Gemini 3 Pro model. Gemini 3.1 Pro also introduced capabilities such as generation of animated SVG files and 3D simulations.

GPT-5 Release (August 2025)

OpenAI released GPT-5, unifying its GPT, GPT-4o and GPT-OSS model families into a single system. The model features faster reasoning, expanded multimodal support and a 256,000-token context window. It boosted improvements in factual accuracy and faster operations but many users felt underwhelmed with the release. 

DeepSeek-R1 Debut (January 2025)

Chinese AI startup DeepSeek launched DeepSeek-R1, a reasoning model trained with reinforcement learning to solve complex analytical problems. Unlike many earlier LLMs, it emphasizes step-by-step logic over text generation. Its release demonstrated China’s growing role in developing advanced machine learning systems.

Google Gemini Launch (December 2023)

Google introduced Gemini, a multimodal model family integrating text, image, audio and code capabilities. Designed to compete with ChatGPT, Gemini was embedded into Google products like Search, Docs and Gmail. The release marked Google’s most significant AI model update since BERT.

ChatGPT Public Release (November 2022)

OpenAI released ChatGPT, a chatbot built on GPT-3.5. Within two months, it reached 100 million users, making it the fastest-growing consumer app at the time. Its popularity introduced generative AI to mainstream audiences and accelerated global adoption.

AlphaGo Defeats Ke Jie (May 2017)

Google DeepMind’s AlphaGo defeated Ke Jie, the world’s top-ranked Go player. The victory proved deep reinforcement learning could outperform humans in one of the most complex strategy games, showcasing machine learning’s capacity for mastering abstract reasoning.

IBM’s Watson Wins Jeopardy! (February 2011)

IBM’s Watson competed on Jeopardy! against two of the show’s most decorated champions — and won — marking the first time most Americans were able to witness an AI system using natural language processing. 

Phrase “Deep Learning” Coined (2006)

Geoffrey Hinton and colleagues popularized the term “deep learning” to describe multilayered neural networks. This terminology helped distinguish modern machine learning approaches from earlier symbolic AI and cemented neural nets as a dominant research direction.

IBM’s Deep Blue Beats Garry Kasparov (1997)

IBM’s Deep Blue defeated world chess champion Garry Kasparov in a six-game match. This was the first time a machine defeated a reigning world champion under standard chess rules, highlighting the growing power of machine computation in problem-solving.

Invention of NetTalk (1985)

Researcher Terry Sejnowksi created an artificial neural network of 300 neurons and 18,000 synapses. Called NetTalk, the program babbled like a baby when receiving a list of English words, but could more clearly pronounce thousands of words with long-term training. 

Creation of Explanation-Based Learning (1981)

Gerald Dejong explored the concept of explanation-based learning (EBL). This approach involved providing a computer with training data, which it then analyzed to develop a rule for filtering out unnecessary information. The idea is that this data is to a computer what prior experience is to a human being.

Creation of Perceptron (1957)

Frank Rosenblatt developed the Perceptron, an early neural network capable of pattern recognition. While initially limited, it laid the groundwork for future developments in neural architectures that drive today’s deep learning models.

Creation of Turing Test (1950)

Alan Turing invented the Turing Test, which consists of three terminals — a computer-operated one and two human-operated ones. The goal is for the computer to trick a human interviewer into thinking it is also human by mimicking human responses to questions. At the time, the test sparked debate around whether computers possess artificial intelligence.   

Frequently Asked Questions