What is Machine Learning?

Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data"

In order to make predictions or decisions without being explicitly programmed to do so.:2 Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks.

It employs techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, and information science. Turing award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational, and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge. Machine learning is closely related to computational statistics, which focuses on making predictions using computers.

In practice, it can turn out to be more effective to help the machine develop its own algorithm, rather than have human programmers specify every needed step. The discipline of machine learning employs various approaches to help computers learn to accomplish tasks where no fully satisfactory algorithm is available. In cases where vast numbers of potential answers exist, one approach is to label some of the correct answers as valid.

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Which AWS Certifications Exist for AI? A Practical Map

If you want to prove you can work with AI on AWS, the certification catalog can feel crowded. This post gives you a map: which credentials are aimed at AI, who each one is for, what the exams look like, and in which order a sensible person would take them. Every figure below comes from the official AWS certification pages as they stood on 25 October 2026, so always double-check the live pages before you book anything. The short version Two certifications are built squarely around AI on AWS, and they sit at opposite ends of the difficulty scale: AWS Certified AI Practitioner (AIF-C01) is the foundational, concept-level exam. AWS Certified Generative AI Developer – Professional (AIP-C01) is the advanced, build-it-in-production exam. Around them sits a ring of neighbouring certifications that make good stepping stones, plus a newer business-focused AI credential and a set of hands-on "Demonstrated" credentials. At a glance Detail AI Practitioner GenAI Developer ...

Artificial Neural Network

Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. The data structures and functionality of neural nets are designed to simulate associative memory. Neural nets learn by processing examples, each of which contains a known "input" and "result," forming probability-weighted associations between the two, which are stored within the data structure of the net itself. (The "input" here is more accurately called an input set, since it generally consists of multiple independent variables, rather than a single value.) Thus, the "learning" of a neural net from a given example is the difference in the state of the net before and after processing the example. After being given a sufficient number of examples, the net becomes capable of predicting results from inputs, using the associations built from the example set. If a feedback loop is prov...

Project: Heartbeat computing

In computer science, a heartbeat is a periodic signal generated by hardware or software to indicate normal operation or to synchronize other parts of a computer system. Usually, a heartbeat is sent between machines at a regular interval in the order of seconds; a heartbeat message. If the endpoint does not receive a heartbeat for a time—usually a few heartbeat intervals—the machine that should have sent the heartbeat is assumed to have failed. Heartbeat messages are typically sent non-stop on a periodic or recurring basis from the originator's start-up until the originator's shutdown. When the destination identifies a lack of heartbeat messages during an anticipated arrival period, the destination may determine that the originator has failed, shutdown, or is generally no longer available. Heartbeat messages may be used for high-availability and fault-tolerance purposes.

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