ML definition
Machine learning (ML) is a branch of artificial intelligence in which software learns patterns from data instead of being programmed with explicit rules. A training algorithm adjusts a model's parameters using historical examples, and the trained model then makes predictions or decisions on new data, such as forecasting demand or flagging a fraudulent payment.
How does machine learning work?
A machine learning project starts with a question data can answer, such as "will this customer cancel next month?" Engineers gather historical records with known outcomes, clean them and turn raw fields into features. A training algorithm, for example gradient-boosted trees in XGBoost or a neural network in PyTorch, repeatedly adjusts the model's parameters to reduce its error. The model is then tested on records it has never seen before it goes live.
- Define the prediction and the metric that measures success.
- Collect and label data, then split it into training, validation and test sets.
- Engineer features and train several candidate models.
- Evaluate on held-out data and compare against a simple baseline.
- Deploy behind an API and monitor accuracy as new data arrives.
Types of machine learning
Most business problems fall under supervised learning, because companies usually hold historical outcomes to learn from: invoices that were paid late, customers who left, machines that failed. Unsupervised methods are used more for exploration and monitoring, while reinforcement learning appears mainly in robotics, games, recommendation tuning and the alignment of language models with human preferences.
- Supervised learning: trains on labeled examples to predict a known target, such as price or churn.
- Unsupervised learning: finds structure in unlabeled data, such as customer segments or anomalies.
- Reinforcement learning: learns which actions to take by maximizing a reward through trial and error.
- Self-supervised learning: creates its own labels from raw data, the method used to pretrain large language models.
Machine learning vs AI vs deep learning
Artificial intelligence is the broad goal of making software perform tasks that need human judgment. Machine learning is the most widely used way to get there, learning from data rather than rules. Deep learning is a subset of machine learning that uses many-layered neural networks and excels on images, audio and text. For structured business data in tables, classic methods such as gradient boosting often match deep learning at a fraction of the cost.
Examples and practical limits
Common examples include product recommendations, credit scoring, spam filtering, predictive maintenance on factory equipment and demand forecasting for retail. In each case the model replaces a rough rule of thumb with a score learned from thousands of past outcomes, and a person or a business rule decides what to do with that score.
The limits are mostly about data. A model cannot learn a pattern absent from its training set, it inherits any bias in historical decisions, and it degrades when real-world behavior shifts. Nexzem's machine learning work starts with a data audit, because data quality decides results more often than the choice of algorithm does.
Common machine learning tools
- scikit-learn, XGBoost and LightGBM for models on tabular data.
- PyTorch and TensorFlow for neural networks.
- MLflow or Weights & Biases for experiment tracking and model versioning.
- Amazon SageMaker, Google's Gemini Enterprise Agent Platform (formerly Vertex AI) and Azure Machine Learning for managed training and hosting.
- pandas and Polars for data preparation, and Jupyter notebooks for exploration.