Learn what machine learning is, what programming languages are used, and how to get started with ML. Simple explanations and examples for beginners.
What is machine learning?
Machine learning (ML) is a technology that allows computers to learn from experience and automatically improve their performance without explicit programming. Imagine that you want a computer to learn to recognize cats in pictures. Instead of writing rules for each part of a cat’s body, you simply show the computer a set of images of cats and non-cats, and it learns to find the differences on its own.
Machine learning can be compared to the work of a teacher explaining something to a student. However, in this case, the student is a computer, and the explanations are data. Depending on the type of task, a computer can learn to classify objects, predict values, find hidden connections, and much more.
Machine learning is divided into several types:
- Supervised learning — when we have data and known answers. For example, predicting the price of a house based on its characteristics.
- Unsupervised learning — when the answers are not known in advance, and the model’s task is to find hidden patterns. For example, clustering users by behavior.
- Reinforcement learning — when a model learns through interaction with its environment and receiving rewards. This is, for example, a method used to train robots or game AI.
What programming languages are used?
In fact, machine learning does not require any special, super-complex programming languages. Most specialists use languages that are already familiar to many:
- Python — the most popular language for machine learning due to its simplicity and huge number of libraries, such as scikit-learn, TensorFlow, PyTorch, and others. Python is often chosen for its readability, which is especially important for beginners learning the basics.
- R — often used for data analysis and statistics. This language has powerful tools for data visualization, which helps to gain a deeper understanding of the processes taking place in the model. It is also suitable for creating machine learning models, especially in an academic environment.
- JavaScript — with libraries such as TensorFlow.js, machine learning can be applied in web applications. This opens up opportunities for developing interactive and educational projects directly in the browser, making machine learning more accessible to a wider range of people.
In addition to these languages, Java, C++, and others are also used, especially in high-performance systems that require maximum speed.
Simple examples for beginners
Machine learning sounds complicated, but let’s break it down with some simple examples:
Example: Weather forecast
Imagine you want to predict whether it will be sunny tomorrow. You have weather data for the last few weeks — temperature, precipitation, pressure, etc. You can create a simple machine learning model that will try to predict tomorrow’s weather based on this data. The main idea here is that the model “learns” from previous data and tries to make the best prediction based on the experience it has gained.
This model can also be used for more complex tasks, such as predicting air pollution levels or assessing the risk of natural disasters.