What Is Machine Learning? Complete Guide to AI, Types, Algorithms & Applications

Machine learning (ML) is a branch of artificial intelligence in which software learns patterns from data instead of following rules written by hand. Google’s developer course defines it as the process of training a piece of software, called a model, to make useful predictions or generate content from data. The term dates back to IBM researcher Arthur Samuel, who used it in a 1959 paper on a program that learned to play checkers. In traditional software a developer writes the rules; in ML the rules are estimated from examples.

ML is behind product recommendations, spam filters, fraud alerts, speech recognition and many other everyday systems.

How Machine Learning Works

A model is trained on example data. During training it adjusts its internal parameters to reduce its errors, and once trained it applies what it learned to new data it has not seen. More data often helps, but only if the data is accurate and representative of the cases the model will face.

The Machine Learning Workflow

1. Data collection: gather relevant data from databases, sensors, application logs or user interactions.
2. Data preparation: clean the data, fix errors and inconsistencies, and convert it into a usable format.
3. Model selection: choose an approach suited to the problem, such as a decision tree, a neural network or a support vector machine.
4. Training: fit the model to the training data.
5. Evaluation: test it on held-out data using metrics such as precision, recall and F1-score for classification tasks.
6. Deployment: integrate the model into an application or business process.
7. Monitoring: track performance over time and retrain when the data or conditions change.

Types of Machine Learning

Supervised Learning

The model learns from labeled examples, where the correct answer is known. Typical tasks are regression (predicting a number, such as a house price or delivery time) and classification (assigning a category, such as spam or not spam, or identifying objects in images). It is the most common approach in business applications, with uses such as credit scoring and demand and price prediction.

Unsupervised Learning

The model looks for structure in unlabeled data. Clustering, which groups similar records together, is used for customer segmentation; other uses include anomaly detection and market basket analysis. Common techniques include k-means clustering and principal component analysis (PCA).

Semi-Supervised and Self-Supervised Learning

Semi-supervised learning combines a small amount of labeled data with a larger pool of unlabeled data. It is useful when labeling every example would be too expensive or slow.

Self-supervised learning creates its training signal from the data itself, for example by hiding words in a sentence and training the model to predict them. Google’s BERT was pre-trained this way on unlabeled text, and the same principle underlies the pre-training of today’s large language models. The practical benefit is less dependence on expensive hand-labeled data.

Reinforcement Learning

An agent learns by acting in an environment and receiving rewards or penalties, gradually finding a strategy that maximizes reward. It is used in robotics, game-playing systems and some control and scheduling problems. DeepMind’s AlphaGo is a well-known example that combined approaches: it first learned from thousands of expert Go games and then improved by playing against itself, before defeating world champion Lee Sedol 4–1 in 2016.

Google’s course also lists generative AI, models that create new text, images, code or other content, as a distinct category of ML system.

Common Algorithms

• Linear regression: predicts continuous values such as prices.
• Logistic regression: binary classification, such as spam detection.
• Decision trees: easy to interpret; used for classification and regression.
• Random forest: combines many decision trees to improve accuracy and reduce overfitting.
• Gradient-boosted trees: build decision trees in sequence, each correcting the errors of the previous ones; widely used on tabular business data.
• Support vector machines (SVM): find boundaries between classes, including complex ones.
• K-nearest neighbors (KNN): classifies a data point by the labels of the most similar points.
• Neural networks: layers of connected units loosely inspired by the brain; the basis of deep learning.

Machine Learning vs AI vs Deep Learning

• Artificial intelligence: the broad field of building systems that perform tasks associated with human intelligence. Not all AI uses ML; simple rule-based systems also count.
• Machine learning: the subset of AI that learns from data rather than hard-coded rules.
• Deep learning: the subset of ML that uses neural networks with many layers. It underpins speech recognition, image recognition and large language models such as ChatGPT.

Deep Learning Architectures

Deep learning works well on unstructured data such as images, audio, text and video, where hand-crafting features is difficult.

• Convolutional neural networks (CNNs) are widely used in computer vision: facial recognition, medical image analysis and vehicle perception.
• Recurrent neural networks (RNNs) were the standard for sequences such as speech and text; transformers have largely replaced them for language tasks and are the architecture behind large language models such as GPT, Claude and Gemini.

Where Machine Learning Is Used

• Healthcare: analyzing medical images, predicting patient risk and supporting drug discovery. A DeepMind and Moorfields Eye Hospital system, for example, could recommend referrals for more than 50 sight-threatening eye diseases as accurately as leading experts.
• Finance: fraud detection, credit scoring and risk analysis.
• Retail and media: recommendations at companies such as Amazon, Netflix and Spotify, plus demand forecasting, pricing and inventory planning.
• Transportation: perception systems in driver-assistance and self-driving vehicles, and route planning in logistics.
• Cybersecurity: detecting malware, phishing and unusual network activity that signature-based tools miss.

Tools and Platforms

• Frameworks: TensorFlow (developed by Google), PyTorch (created at Meta and now hosted by the PyTorch Foundation under the Linux Foundation), scikit-learn for classical ML, and Keras as a high-level API.
• Managed cloud platforms: Amazon SageMaker, Google Vertex AI, Azure Machine Learning.
• Hardware: GPUs (mainly NVIDIA) and Google’s TPUs for training and serving large models.

If you need outside help building ML systems, you can browse AI and ML development providers in our directory.

Challenges

ML systems depend on data quality, and biased or unrepresentative training data leads to biased results, which matters in lending, hiring and healthcare. Complex models can be hard to explain, which complicates audits and regulatory review. Training large models is computationally expensive, and models degrade as real-world data drifts, so they need ongoing monitoring and retraining. Using personal data also raises privacy concerns.

Techniques that address these problems include explainable AI methods, differential privacy, federated learning, which trains on-device and sends only model updates rather than centralizing raw data, and automated machine learning (AutoML) for faster, more consistent model building.

Where It Is Heading

Current development centers on generative AI and large language models, multimodal models that combine text, images and audio, running models on edge devices rather than in the cloud, and AI agents that carry out multi-step tasks; quantum machine learning remains at an early research stage. Each of these still relies on the same fundamentals described above: good data, a clear objective, careful evaluation and ongoing monitoring. For a closer look, see our article on machine learning trends in 2026.