Machine Learning

Machine learning is the set of methods and techniques that allow a machine (a computer) to learn autonomously from data. From there, it becomes clear that artificial intelligence and machine learning are tightly linked.

Machine learning is one of the building blocks of AI. The progress of AI is largely driven by progress in machine learning. Conversely, traditional programming consists of giving a machine precise instructions following predefined rules. Machine learning is autonomous; programming is dependent on human instructions.

Often handled by Data Scientists, machine learning is rooted in using large datasets — hence its strong link with Big Data. It relies on algorithms that discover, from a dataset, recurring patterns called patterns.

By uncovering these patterns, the algorithms use their own outputs to learn, evolve and improve (hence « learning »). Developing a machine learning algorithm typically involves four steps:
- Prepare the training ground: provide the model with a clean dataset on which it will base its learning (photos, numbers, words, etc.). The quality of the training data determines the quality of what the algorithm learns.
- Pick the trainee algorithm: a number of machine learning algorithm families are tailored to different use cases. Among them: regression, linear, clustering algorithms, neural networks (this is what's called [Deep Learning](/ressources/glossaire-de-la-tech/deep-learning)).
- Train it: a form of calibration. You run the chosen algorithm over the prepared dataset and compare its outputs to expected results.

If the gap is too wide, you tune the model until you reach the expected outcome. The model is now calibrated and ready to be used in its domain. Example use case: spotting spam emails.