Machine learning, or automatic learning, is the main technique of AI. It consists of training algorithms from a learning base to enable them to make predictions or automate tasks.
What is machine learning?
Machine learning or automatic learning is a sub-discipline of artificial intelligence which consists in detecting trends within a history of knowledge (example: the climate) to derive a prediction model for the future (here, weather).
What is the interest of machine learning?
One of the main interests of machine learning is to automate tasks. Among the most popular applications of machine learning are product recommendation, machine translation, autonomous vehicles and diagnostic assistance in the health sector.
Within massive volumes of data, machine learning can also detect hidden trends, which are not detectable through human analysis.
Who uses machine learning?
Machine learning is used in many areas:
- The automobile industry, with in particular the autonomous car,
- Consumer goods, via product recommendation,
- Finance, via financial risk prediction models in particular,
- Transport, via route calculation applications,
- Health, via diagnostic assistants,
How does machine learning work?
Machine learning (ML) is one of the main artificial intelligence technologies. It consists of training an algorithm to recognize recurring patterns within a learning base. This training results in a computer model designed to make predictions (recognize a sound, an image, etc.) or automate tasks (answer a question, automate driving a vehicle, etc.).
Where a traditional program executes instructions, a machine learning algorithm improves its performance as it learns, but also over the evolution of the context and successive retraining. The more data we “feed” it, the more accurate it becomes.
To create its learning model, machine learning uses statistical algorithms or neural networks. In the 2010s, machine learning reached a momentum with the advent of big data and the progression of computing capacities (and in particular the rise 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 the difference between machine learning and artificial intelligence?
Artificial intelligence aims to simulate the human mind. From there, machine learning is only one of the tools to achieve this goal. It allows the machine to ingest examples according to objectives to be achieved, for example images or videos to recognize a pedestrian crossing in the case of an autonomous car. But this technique has its limits. It does not allow complex reasoning to be carried out. It is therefore necessary to couple it with other methods to move towards an AI worthy of the name.
Certainly very powerful in terms of learning, neural networks are however not completely reliable and can in some cases lead to biased or illogical results (example: an autonomous car taking a roundabout in the wrong direction). Hence the interest of combining deep learning with other methods, a symbolic AI or expert system for example based on predefined business rules, the highway code in our example, which will be injected into the network to refine reasoning skills.
What is a machine learning model?
A machine learning model is a file that has been trained from a learning base in order to automate tasks, for example recognizing an emotion in view of an expression on a face, translating a text, proposing produced 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.
Supervised vs unsupervised machine learning, what’s the difference?
On the supervised machine learning side, the training data is previously annotated or labeled. Objective: to use a representative learning base which makes it possible to arrive at a model capable of generalizing, that is to say of then making 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.
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.
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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