Machine learning (ML) is a subset of artificial intelligence (AI) that enables computers to perform tasks by learning from data, without being explicitly programmed for each specific rule or outcome. It uses algorithms — defined sets of instructions — to identify patterns, make predictions, and improve performance over time.

In enterprise settings, machine learning is commonly used to automate processes, improve decision-making, and extract insights from large volumes of data. Its capabilities typically fall into three categories:

  • Descriptive: Analyzes historical data to explain what has happened.
  • Predictive: Uses patterns in data to forecast future outcomes.
  • Prescriptive: Recommends actions based on data-driven predictions.

Machine learning models are trained on large datasets to learn how to generate accurate outputs or actions. The more relevant data the system is exposed to, the better it becomes at performing tasks with minimal human intervention. This capacity for continuous learning and adaptation makes machine learning valuable across industries such as finance, healthcare, and retail, from fraud detection and patient risk scoring to demand forecasting and customer personalization.

Types of machine learning

There are several types of machine learning, each using different algorithms depending on the task and the available data.

Supervised machine learning

Supervised learning trains a model using labeled data — data that includes known outcomes or categories. The model learns to map inputs to the correct outputs, enabling it to make accurate predictions or classifications when exposed to new data. For example, email spam filters use supervised learning to distinguish between spam and legitimate messages based on historical examples.

Unsupervised machine learning  

In unsupervised learning, the model works with unlabeled data — data that has not been assigned predefined categories. It identifies hidden patterns, structures, or groupings within the dataset. Common use cases include clustering similar customer segments or detecting anomalies, such as suspicious activity on a bank account. Unsupervised learning is valuable for data exploration and uncovering insights that may not be apparent through manual analysis.

Reinforcement machine learning

Reinforcement learning teaches a model to make decisions through trial and error within a defined environment. The model receives feedback in the form of rewards or penalties based on its actions, gradually learning which behaviors lead to optimal outcomes. Over time, it can perform complex tasks autonomously. This approach is often used in environments that require sequential decision-making, such as managing treatment strategies in healthcare.

How does machine learning work?

Machine learning uses algorithms to convert raw data into insights or actions. These algorithms enable a system to learn from training data — examples of input-output relationships — and improve performance over time. As the model processes more relevant data, it becomes more accurate and efficient in completing specific tasks.

Training & learning process

Before a machine learning model can perform reliably, it must be trained using a structured process:

  • Data collection: The first step is gathering data from relevant sources, such as databases, images, audio, or public datasets. This data forms the foundation for identifying patterns and learning behaviors.
  • Data processing: Raw data must be cleaned and prepared for use. This involves removing errors or inconsistencies, addressing missing values, and distinguishing between labeled and unlabeled data depending on the ML approach. High-quality, well-structured data is critical for accurate outcomes.

During training, the model adjusts its internal parameters — numerical values the model uses to learn — in response to the data. This process also helps identify potential issues like:

  • Overfitting: When the model performs well on training data but fails to generalize to new data.
  • Underfitting: When the model performs poorly on both training and unseen data due to insufficient learning.

Feature engineering & model optimization

Once the data is prepared, additional steps help refine the model’s performance:

  • Feature engineering: This involves identifying and selecting the most relevant variables (features) from the dataset to improve the model’s predictive accuracy. Features should be consistent across training and test data to ensure reliability. For example, in a healthcare use case, relevant features might include patient age, diagnosis codes, or lab results.
  • Hyperparameter tuning: Hyperparameters are configuration settings that control how the model learns (e.g., learning rate and tree depth). Adjusting these — either manually or using automated tools — can significantly enhance model performance, similar to tuning a camera for optimal exposure.

How models improve over time

Machine learning models improve as they are exposed to new data and outcomes. They use feedback loops to refine decision-making, for example, identifying whether a prediction was correct or not.

However, performance can degrade over time due to model drift, which occurs when changes in data patterns reduce the model’s accuracy. Ongoing evaluation is essential. This includes:

  • Testing the model against unseen data
  • Monitoring key performance metrics such as accuracy, precision, and recall
  • Re-training or tuning the model based on performance trends

Regular assessment ensures the model continues to deliver reliable results aligned with business goals, whether it’s forecasting retail demand or supporting clinical decision-making.

Machine learning use cases

Machine learning has a wide range of practical applications, driven by its ability to learn and adapt over time without explicit reprogramming.

It is commonly used for:

  • Personalized content and recommendations: Streaming platforms and social media apps use machine learning to suggest relevant content by analyzing user behavior and predicting preferences. In retail, similar techniques support personalized product recommendations and dynamic pricing.
  • Virtual assistants and speech recognition: Voice assistants like Siri, Alexa, and Google Assistant use ML for natural language processing (NLP), enabling speech-to-text transcription and intent recognition. These capabilities also support voice-driven customer support systems in sectors like healthcare and financial services.
  • Customer service automation: ML powers chatbots, online self-service tools, and tailored customer interactions. By learning from previous conversations and user behavior, these systems can continuously improve response accuracy and customer experience.
  • Finance and banking: Machine learning is widely used for fraud detection, identifying unusual patterns in transactions compared to an individual’s normal spending habits. It also supports credit scoring, risk modeling, and automated financial advice.