Artificial Intelligence VS Machine Learning VS Deep Learning

Many businesses use what they believe to be one technology when they are actually using a different one! For instance, 40% of European startups who advertise that they apply AI don't actually do so. There is a lot of uncertainty since artificial intelligence and machine learning along with deep learning are such broad fields.

The order in the title — AI then ML then DL — is just right; it just happened that Deep Learning is a subfield of Machine Learning, which, in its turn, is Artificial Intelligence’s subfield.

Here, we won’t repeat ourselves explaining the terms all over. See the links to other definitions at the bottom of the page.

Now the question is, what is the relationship between AI and ML and DL and whether they can really be opposed — or juxtaposed?

AI vs ML

In a broad sense, AI is not a technology but an advancement (a study) that aims to mimic human intelligence. The AI systems do not need pre-programming; instead, they use such algorithms which can work with their own intelligence.

In contrast, ML enables a computer system to make decisions or predictions based on historical data. In order for a machine learning model to provide reliable findings or make predictions from historical data, a vast quantity of structured and semi-structured data is used. 

An algorithm used in machine learning uses past data to self-learn. It only works for certain domains; for example, if we build an ML model to find boats in photos, it will only detect boats. If we add additional data, such as a photo of a bus, the model will no longer give satisfactory results.


  • AI is a study aimed at creating a clever computer system that can solve complicated issues, just like humans.
  • ML is the area of computer science which trains machines to learn from data, execute certain tasks, and provide an accurate output.

ML vs DL

Machine Learning uses a range of algorithms and techniques that analyze and process data. These algorithms can be used for tasks such as classification, regression, clustering, and prediction and can be applied to various industries. Ultimately, the best approach will depend on the specific requirements of the task at hand.

Deep Learning models use artificial neural networks inspired by the human brain. These models are often used for tasks that involve processing large amounts of data or recognizing complex patterns. They tend to be more accurate and perform better than other types of machine learning algorithms.

The best parameters of ML models are chosen under human supervision, meanwhile, DL models have more advanced optimization algorithms. ML model optimization involves selecting the best model parameters, whereas in DL, only model hyperparameters are chosen, and then the model optimizes itself via a backpropagation algorithm.


  • ML model uses parameters chosen by humans and is optimized as more data is added by humans.
  • DL model only uses hyperparameters and optimizes itself. DL models are capable of processing more data compared to ML models and providing more accurate results.
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