Understand ML in AI

Machine learning

To understand this better, think of machine learning as a way of teaching computers by example rather than instruction. In traditional programming, if the system makes a mistake, you would need to manually adjust the rules. In machine learning, the model continuously learns and improves by adjusting itself based on errors and feedback, becoming more accurate over time. Machine learning models are essentially algorithms (or mathematical formulas) that find patterns in data.

Example, spam filters

In traditional programming, you would create a set of explicit rules for identifying spam, like flagging certain keywords (“prize,” “win,” “free”). The program would then follow these rules to classify emails. In ML you might show the system thousands of labeled examples of spam and non-spam emails. Over time, it would learn the patterns on its own, noticing certain phrases, words, or formats that are common in spam emails without needing explicitly programmed rules.

Types of ML

There are several types of machine learning, each suited to different types of tasks and goals. Each type of machine learning has unique strengths and is chosen based on the task at hand. Supervised learning, for example, is great for prediction tasks, while unsupervised learning is used for exploring and finding hidden patterns in data. Machine learning has become central to AI because it allows systems to adapt, generalise, and improve over time.

Supervised learning

The model is trained on labeled data, where each example has both an input (like an image) and an output (like the label “cat”).
Supervised learning is commonly used in applications like spam detection, fraud detection, and medical diagnosis.

Unsupervised learning

Here, the model works with unlabelled data and looks for patterns on its own. It’s commonly used for tasks like customer segmentation, where the model groups similar customers together without specific labels.

Reinforcement learning

In this approach, the model learns by interacting with an environment and receiving feedback in the form of rewards or penalties. It’s used in applications like game-playing AI (such as AlphaGo) and robotics.

Machine learning in action

Machine learning powers a lot of the AI systems we interact with daily, even if we don’t always realise it. Think of AI as the broad goal, creating machines that can perform intelligent tasks. Machine learning is one approach to achieving this goal, enabling machines to improve over time by learning from data. Without machine learning, AI would be limited to simpler tasks that don’t require adaptation or improvement over time. With machine learning, AI can tackle complex, data-driven tasks and continuously refine its performance, bringing us closer to building systems that truly mimic human intelligence.

Recommendation engines

Platforms like Netflix, Amazon, and YouTube use machine learning to recommend content based on what users have watched or purchased before.

Voice assistants

Siri, Alexa, and Google Assistant use ML to understand spoken language and continuously improve their accuracy with usage.

Image recognition

Apps that identify objects, plants, or even faces (like Google Photos or Snapchat filters) rely on ML to analyse and recognise visual patterns.

Fraud detection

Financial institutions use ML models to spot unusual transaction patterns and detect potential fraud in real time.