What is Machine Learning?

Having defined what artificial intelligence is in a previous article, it's time to delve into one of its most central and fascinating fields: machine learning.

Often cited in discussions about AI, and sometimes confused with it, machine learning is nevertheless a very specific concept, with very concrete applications. In this article, we explain what it is, how it works, what it's for, and why everyone's talking about it.

What is machine learning?

Machine learning is a branch of artificial intelligence that enables a machine to learn from data, without being explicitly programmed for each task.

Instead of manually coding all the rules needed for a machine to perform an action (such as recognizing a cat in a photo), a machine learning algorithm is given a large number of examples (photos of cats, in this case), and deduces the rules on its own. It learns to spot patterns, common features and recurrences.

In a nutshell: machine learning means learning from experience.

The different types of learning

There are several types of machine learning, each with its own specific features:

1. Supervised learning

This is the most commonly used. The algorithm is provided with input/output data: for example, images (inputs) with labels indicating what they represent (outputs). The aim is for the algorithm to learn to generalize to predict the output of new data.

Example: Predict whether an email is spam or not based on its content.

2. Unsupervised learning

There are no ready-made answers here. The algorithm only receives raw data, and has to extract structures or groupings from it (this is called clustering).

Example: Automatically group customers of an e-commerce site according to their purchasing habits.

3. Reinforcement learning

In this case, the algorithm learns to act in an environment, reaping rewards or penalties depending on the actions it performs. It improves its behavior over time to maximize its overall reward.

Example: A robot that learns to walk or an AI that learns to play a video game.

How does it work?

The machine learning process generally follows several key stages:

1. Data collection: the more relevant data there is, the better the algorithm can learn.
2. Cleaning and preparation: data must be formatted and cleaned.
3. Choice of algorithm: depending on the task (classification, regression, clustering, etc.).
4. Training: the algorithm is trained on an example dataset.
5. Testing and validation: we check that it generalizes to new data.
6. Continuous improvement: adjusting, adding data, optimizing.

This cycle is at the heart of many modern applications.

Concrete examples

Here are a few areas where machine learning is used on a daily basis:

• Healthcare: AI-assisted diagnostics, disease detection on medical imaging
• Finance: fraud detection, risk analysis, automated trading
• E-commerce: product recommendations, customer segmentation
• Transportation: route optimization, autonomous vehicles
• Industry: predictive maintenance, automated quality control

And of course, you're also in contact with it through your Google searches, Netflix suggestions and e-mail spam filters.

Why is it important to understand machine learning?

Machine learning isn't just for data scientists and engineers: it's already shaping our world. Understanding its basic principles will enable you to :

• Demystifying technology
• Identify opportunities and limits
• Better interaction with everyday tools
• Participate in the societal and ethical debates it raises

So how do you get started?

The best way to understand machine learning is to manipulate it. At Learning Robots, we've developed AlphAI, a tool that enables students, teachers and professionals to visualize and test machine learning mechanisms in an intuitive way, with or without code.

Want to know more? Explore our solution at https://www.learningrobots.ai/solution-alphai

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