Machine learning: definition, model, algorithm and language

Machine learning is a machine learning technique used in artificial intelligence. It consists of training models from a knowledge base in order to perform complex tasks.

Machine learning (ML), or machine learning, is one of the main artificial intelligence technologies. It makes it possible to make predictions based on a model trained from a data history that may change over time. Where a traditional program executes instructions, a machine learning algorithm improves its performance as it learns. The more data we “feed” it, the more accurate it becomes.

To describe its learning model, machine learning uses statistical algorithms or neural networks. In the 2010s, machine learning reached momentum with the advent of big data and the progression of computing capacities (and in particular the rise in power of GPUs). Big data is indeed essential to train models on the vast volumes of data necessary for automatic language processing or image recognition.

What is a machine learning model?

A machine learning model is a file that has been trained from a knowledge base in order to automate tasks, for example recognizing an emotion in view of an expression on a face, translating a text, proposing products according to a palatability profile… Once trained, the model must be able to generate results from data (texts, photos) that it has never processed before.

What is the relationship between AI and machine learning?

L’artificial intelligence aims to give a machine the ability to reason and behave like a human. Machine learning is only one way to help move towards this vision. Alongside machine learning, there are other AI techniques including expert systems, simulation and digital twins.

What are the main machine learning algorithms?

A distinction is made between supervised machine learning algorithms and unsupervised machine learning algorithms. On the supervised learning side, the training data is previously annotated or labeled. Objective: to use a database representative learning process which makes it possible to arrive at a model capable of generalizing, that is to say of then carrying out correct predictions on data not present in the initial learning base. In the field of supervised learning, we find classification algorithms, linear regression, logistic regression, decision trees, or even random forests.

As for unsupervised learning, it decodes the context information of the training data and the logic that derives from it, without resorting to a pre-established source of knowledge. The data is neither annotated nor labeled. In this category are clustering algorithms (like K-means) designed to divide data into similar groups. They can, for example, make it possible to group together by type of customer, according to profile characteristics, similar purchasing behavior, etc.

Reference articles

What do we expect from a machine learning engineer profile?

In machine learning, the basics in computer science and mathematics must be solid. The technical expertise of any engineer profile includes mastery of Python and C++ languages, such as PyTorch and TensorFlow frameworks. Fluency in English is essential, and advanced knowledge of Git and Docker solutions are highly appreciated. On a personal level, you have to be organized, work methodically, enjoy challenges, learn from mistakes, be determined, etc.

What is the place of Python in machine learning?

The Python language has established itself as the reference language for machine learning applications. Candidates trained in C++ are often forced to change their code habits.

Machine learning vs deep learning: what’s the difference?

Deep learning is a subfield of machine learning, which uses a neural network inspired by the human brain system, and which requires a lot of data and computing power to train. Suitable for both supervised learning and unsupervised training, it is mainly used in visual or sound recognition.

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Machine learning: definition, model, algorithm and language


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