How do machine learning algorithms work?

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Machine learning is an exciting discipline that has revolutionized many fields thanks to its intelligent algorithms. But how do these algorithms actually work? How do they manage to learn and make decisions without being explicitly programmed for each task?

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What is machine learning?

In a nutshell, machine learning is a branch of artificial intelligence (AI) that enables computers to learn from data without being explicitly programmed.

The basics of the machine learning algorithm

Defining machine learning algorithms

A machine learning algorithm is first and foremost a mathematical procedure that enables a computer to learn from data. It can then use this knowledge to make decisions or solve problems without being explicitly programmed for each task.

Machine learning fundamentals

Machine learning algorithms are based on two fundamental principles: learning from data and generalizing from new data.

Types of machine learning algorithms

There are several types of machine learning algorithms, each with its own characteristics and applications.

Supervised algorithms

Supervised algorithms are used to solve classification and regression problems by learning from labeled examples. They are "supervised" because they use training data with labels to fit their model.

For example, logistic regression algorithms can be used to predict a prospect's probability of conversion based on different marketing variables such as age, gender and online behavior.

Unsupervised algorithms

Unsupervised algorithms, on the other hand, do not use labels in their training data. They are used to discover patterns, structures or groupings in the data.

For example, clustering algorithms can be used to group customers into homogeneous segments based on their purchasing behavior, enabling personalized offers and marketing messages.

Semi-supervised algorithms

Semi-supervised algorithms combine elements of both supervised and unsupervised algorithms. They use a small set of labeled data and a large set of unlabeled data to perform their training.

For example, semi-supervised algorithms can be used to classify customer-generated data according to interest and conversion potential: labeled data (e.g. previous purchases) and unlabeled data (e.g. site navigation data).

Reinforcement algorithms

Reinforcement algorithms are used in situations where an agent interacts with an environment and learns to maximize a given reward. They are often used for machine learning in games or autonomous robots.

For example, reinforcement algorithms can be used to optimize pricing or ad budget allocation decisions, by learning which actions generate the best rewards, such as conversion rates or revenues.

How do machine learning algorithms work?

Now that we have an idea of the different types of machine learning algorithms, let's take a look at how they work in general: 

  1. Acquiring training data: The process begins with the acquisition of training data, which are examples of the data on which the algorithm will learn.
  1. Data preparation: Next, the data must be prepared for use in the learning process. This may include steps such as data cleaning, normalization or selection of relevant features.
  1. Learning process: The heart of machine learning lies in the learning process. The algorithm uses the training data to adjust its internal parameters and create a model capable of generalizing to new data.
  1. Algorithm evaluation and adjustment: Once the model has been trained, it is evaluated using independent test data. If performance is unsatisfactory, the algorithm can be adjusted and retrained to improve results.

Examples of commonly used machine learning algorithms

Machine learning algorithms are applied in many fields and have a significant impact on various industries. Here are a few concrete examples:

Netflix recommendation filters

Netflix uses sophisticated machine learning algorithms to recommend movies and series to its users. These algorithms analyze each user's past viewing preferences and compare them with those of other similar users. Based on these comparisons, Netflix can recommend personalized content that matches each individual's tastes.

Amazon recommendation systems

Amazon uses machine learning algorithms to provide personalized product recommendations to its customers. By analyzing purchase histories, preferences and similar buying behavior, Amazon suggests relevant products that match users' interests and needs.

Virtual assistance with Google Assistant

Google Assistant, Google's virtual assistant, uses machine learning algorithms to understand user queries and provide accurate answers. Thanks to machine learning, Google Assistant is able to understand and adapt to natural language, enabling it to answer users' questions in a conversational manner.

These examples illustrate the concrete application of machine learning algorithms in the real world. Companies are using these technologies to improve their products, optimize their operations and offer personalized experiences to their users. Machine learning is opening up new perspectives and helping to shape the future of many industries.

The difference between Deep Learning and Machine Learning

Deep Learning and Machine Learning are two subfields of artificial intelligence (AI) that focus on learning from data. Although they share similarities, they differ in their approach and capabilities. Here are the main differences between Deep Learning and Machine Learning:

Structural differences and complexity

  • Machine Learning is based on algorithms that learn from data and make predictions or classifications. These algorithms generally require manual design and selection of the features relevant to learning. For example, in a classic Machine Learning algorithm, you need to specify certain variables (such as age, gender, etc.) to predict a target variable (such as income).
  • Deep Learning, on the other hand, uses artificial neural networks. These networks are made up of many layers of interconnected neurons, which enable learning directly from raw data, without the need for manual feature selection. This approach enables Deep Learning to capture more complex patterns and relationships in the data.

Difference in learning ability

  • Machine Learning excels at specific tasks such as classification, regression or clustering. It can be used to solve a wide variety of problems, from spam filtering to sales prediction. However, it can reach its limits when dealing with complex or unstructured data.
  • Deep Learning is particularly powerful when it comes to processing complex, unstructured data, such as images, videos or natural language. It can learn hierarchical representations of data, enabling it to perform sophisticated tasks such as image recognition, machine translation or speech synthesis.

Data resources required

  • Machine Learning can work effectively with moderate-sized datasets and reasonable computing resources. It can deliver satisfactory results with less complex models, making it more accessible for many applications.
  • Deep learning, on the other hand, generally requires large quantities of data to build accurate models and avoid overlearning. It also requires considerable computing resources, such as graphics processing units (GPUs), to train and execute deep neural networks.

Kiliba: Optimize your e-mail delivery with machine learning

Kiliba is a marketing automation software specially designed for e-tailers. Thanks to its intelligent use of machine learning algorithms, Kiliba lets you automate and optimize the sending of your marketing e-mails.

With Kiliba, you don't have to worry about manually sending e-mails to your customers. Our machine learning algorithm analyzes your audience data to effectively segment your database. So you can create targeted, personalized e-mail campaigns, sending the right message to the right person.

Our algorithm analyzes your users' engagement in real time, such as open and click rates, or their behavior on your site. Kiliba automatically adjusts sending parameters, such as optimal timing and frequency, to maximize the impact of your campaigns. You're sure to reach your customers when they're most receptive.

And that's not all! Kiliba personalizes the content of your e-mails according to the individual preferences of each recipient. Thanks to this intelligent personalization, you can offer your customers unique experiences, with products, offers and recommendations that match their specific interests.

Kiliba makes e-mailing more efficient and cost-effective. Using the most advanced machine learning algorithms, this solution enables you to automate your e-mail marketing strategy while delivering personalized, relevant communications to your customers. Free up your time and maximize your ROI with Kiliba and its power of artificial intelligence applied to marketing automation.


There, now you have a better understanding of how machine learning algorithms work. They've become indispensable in many fields, offering exciting opportunities and innovative solutions. So dive into the world of machine learning and discover all it has to offer!

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