Let’s discover the differences between a Machine Learning and Deep Learning algorithm

Marcello Politi : 12 November 2025 22:08

In the world of artificial intelligence, we often hear two terms: machine learning (ML) and deep learning (DL). Both are methods for implementing AI through the training of machine learning algorithms, which are then used to make predictions about the future and make decisions.

These two subjects use different approaches and have different capabilities and characteristics. In this article, we’ll explore the differences between the two and better understand how they work.

Machine Learning

Machine learning is the discipline of designing algorithms capable of making predictions by learning patterns from past data. A machine learning algorithm differs from a “classic” computer science algorithm in how the problem is structured.

In a classical algorithm, we give the machine rules that it must follow step by step to transform the input into an output. For example, an input might be a list of numbers [1, 2, 3, 4, 5], the rule might be: “transform each number into its square”, so f(x) = x^2.

Once given these rules and this input, the algorithm would be able to give us the following output: [1, 4, 9, 16, 25].

In a Machine Learning algorithm, however, the problem is turned upside down.

What we provide to the machine is the input and the expected output, so [1, 2, 3, 4, 5] and [1, 4, 9, 16, 25], and the algorithm will give us as output the rule that relates these values, so f(x) = x^2.

This way, all we have to do is collect observations (data) and feed them to the algorithm , which will find the relationship that governs our observations and can then be used to make future predictions.

The type of Machine Learning algorithm just described actually falls into the category of algorithms defined as Supervised Learning .

There are basically 3 different categories which are the following:

1. Supervised Learning : Algorithms in this category, such as Linear Regression or Random Forest, are trained on labeled data, meaning the machine is given a series of inputs associated with outputs, as in the case discussed previously. The algorithm will learn to map the inputs to the outputs by finding the relationship between them.
2. Unsupervised Learning : Unsupervised learning algorithms such as K-Means Clustering or DBSCAN are trained on unlabeled data and are therefore used when we don’t have the outputs associated with the inputs within our dataset. These algorithms have the ability to find structures and patterns within the dataset. For example, imagine collecting data from website users and then wanting to cluster them, that is, divide them into groups to target them and improve product recommendations. This can be achieved with a clustering algorithm that uses machine learning.
3. Reinforcement Learning : In reinforcement learning algorithms, we train an agent (think of a robot, for example) through its interactions with its environment, and it learns through positive or negative feedback. For example, a robot learning to walk will receive negative feedback every time it hits a wall and positive feedback every time it reaches its goal.

There are many machine learning algorithms, and it’s the data scientist ‘s job to figure out which one to use and when. One algorithm in particular, called Artificial Neural Networks, has opened up numerous new possibilities, and AI scientists have begun to focus on studying this single algorithm, thus giving rise to a new field of study called Deep Learning.

Deep Learning

Deep Learning is a subset of Machine Learning , in fact it focuses solely on the use of Artificial Neural Networks that are able to learn hidden patterns within data.

The Artificial Neural Network (ANN) is inspired by the biological one.

The basic element of a biological neural network is the single neuron , while in an artificial one we find the perceptron . The neuron perceives input electrical discharges through its dendrites, which are processed by the nucleus. If the sum of these discharges exceeds a certain threshold, an output electrical discharge will be produced through the axon.

Similarly, the perceptron takes numbers as input, performs a computation on those numbers, and produces an output number, either 0 or 1. For example, the input numbers might represent the values of pixels in a black and white image, and we would like the output to be 1 if the image represents a dog and zero otherwise.

However, the perceptron is too simple an algorithm and has little learning capacity.

So we can create a more complex algorithm by structuring many perceptrons (or neurons) into layers where the output of the neurons in the previous layers will be the input for the subsequent layers.

There are only four layers in this image. We have the first input layer , followed by two hidden layers , and finally an output layer composed of a single neuron. The number of hidden layers is arbitrary, and networks can often be very deep, which is why the term deep is used.

Neural networks differ not only in the number of neurons or layers, but also in the type of computation these layers and neurons perform. There are various types of neural networks, such as Convolutional Neural Networks (CNNs) suitable for visual tasks, or Recurrent Neural Networks (RNNs) for tasks that process sequential data such as time series or natural language.

Differences between Machine Learning and Deep Learning

Now that we’ve covered the main aspects of Machine Learning and Deep Learning, let’s look at the differences between the two and when one should be preferred over the other.

1. Data : Deep Learning algorithms typically require a much larger amount of data than Machine Learning algorithms to be trained properly;
2. Feature extraction : Before training a machine learning model, the input data must be preprocessed and cleaned. During this preprocessing, a fundamental phase is called feature engineering , in which the data scientist attempts to extract new features from the data. For example, if the data refers only to a person’s height and gender, a data scientist might also try to infer the person’s weight to provide additional information to the machine learning algorithm. Deep learning, on the other hand, is able to extract these features automatically without the manual assistance of an expert;
3. Performance : Deep Learning algorithms outperform Machine Learning algorithms in the most complex tasks, but they require large computational capacities and, as mentioned, a large amount of data that is not always available;
4. Interpretability : Machine learning models are generally more interpretable than deep learning models. For example, if a person at a bank is rejected for a mortgage, and the algorithm that made the decision was based on machine learning, it would be possible to explain and justify the decision. However, if deep learning had been used, only the model would know the reason for that decision, which is why they are often called black box algorithms.

Conclusions

Machine learning and deep learning are two approaches to AI. Both have strengths and weaknesses , and the choice depends on the problem being addressed, the availability of data, and the complexity of the task.

Understanding the differences between these two techniques is crucial for AI practitioners and researchers to choose the most appropriate approach for their specific use cases.

Marcello Politi


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