Machine learning

Contents

• What is machine learning
• How machine learning works
• The relationship between artificial intelligence and machine learning
• Types of machine learning
• Applications of machine learning
• How to implement an ML solution

What is machine learning

Machine learning (ML) is a process in which algorithms and models are created that allow computers to learn to solve various tasks independently. This includes a variety of methods and algorithms used for analyzing and processing data, as well as building and evaluating models. With ML, computers can adapt to new data and improve their results without the need for human intervention.

How machine learning works

Traditional programs follow clear instructions that define the sequence of actions to achieve a result. For example, if a program is shown a picture of an elephant, it will memorize that object and be able to recognize it when shown again. However, when trying to identify another elephant in a new picture, the program may fail.

In contrast, machine learning algorithms operate on the principle of learning from examples. The program is shown numerous images of elephants, and it begins to identify key features such as ear shape, trunk length, and body proportions. After this, even if it is shown a new picture, it can recognize the elephant and distinguish between African and Indian elephants.

The relationship between artificial intelligence and machine learning

Artificial intelligence (AI) is a broader concept that encompasses applications capable of performing complex tasks without direct human intervention. AI can operate based on predefined algorithms or learn independently. Machine learning is a subset of AI and is always related to it. However, not every AI solution implies the use of machine learning.

Types of machine learning

There are several main approaches to machine learning:

• Supervised learning: the program is trained on sorted or labeled data that indicates the correct answers. For example, when classifying images of daisies, the program looks for specific features.
• Unsupervised learning: in this case, the algorithm works with unstructured data, identifying patterns and associations. This approach helps in detecting anomalies, for example, in financial transactions.
• Reinforcement learning: here the program receives “rewards” for successful decisions and penalties for mistakes. This method is effective in dynamic environments such as games or driving.
• Ensemble methods: using multiple algorithms simultaneously improves the accuracy and reliability of results.
• Deep learning and neural networks: modern neural networks use vast amounts of data to identify complex patterns and features.

Applications of machine learning

Machine learning is widely applied in various fields, as computers can process large volumes of data more quickly and accurately than humans. Here are some of its applications:

• Image recognition
• Making forecasts and models
• Data-driven decision making
• Optimizing business processes and inventory management

In marketing, machine learning helps:

• Predict consumer behavior
• Personalize content and advertisements
• Analyze market data to offer optimal pricing
• Enhance user experience on websites and applications

How to implement an ML solution

Implementing ML solutions can be a complex and costly process that goes through several stages:

1. Defining the goal and formulating the desired outcome
2. Collecting and preparing data
3. Selecting algorithms and training models based on the tasks
4. Training the model and testing it
5. Integrating the algorithm into business processes

For small companies, there are accessible platforms and services, such as Google Analytics and Yandex.Metrica, that use machine learning to analyze audience behavior and enhance conversion rates. Marketing automation services, such as Mindbox, can assist in audience segmentation and product recommendation generation.