Machine learning
Machine learning (ML) is a subfield of artificial intelligence where computers learn to find patterns in data and make decisions without rigid programming. ML models improve their accuracy as information accumulates.
Contents
- What is machine learning in simple words
- Main tasks of machine learning
- 1. Prediction (regression)
- 2. Classification
- 3. Clustering
- 4. Recommendations
- Main approaches to learning
- 1. Supervised learning
- 2. Unsupervised learning
- 3. Reinforcement learning
- Main stages of developing an ML model
- 1. Data collection and preparation
- 2. Choosing an algorithm and training the model
- 3. Quality assessment
- 4. Deployment into production (MLOps)
- ML tools and technologies
- Programming languages
- Main Python libraries
- The ML engineer profession
- What an ML engineer needs to know
- Where machine learning is applied
What is machine learning in simple words
Machine learning (ML) is a branch of artificial intelligence. Instead of writing a rigid algorithm with a million rules for the computer, it is taught to find patterns on its own based on hundreds of thousands of examples — the same way people learn.
How this works with a simple example: to teach a computer to distinguish cats from dogs, we load into it 10,000 photos of cats and 10,000 photos of dogs, labeled. The algorithm analyzes the pixels, finds common features and itself understands what a cat looks like and what a dog looks like. In the future, it will unmistakably recognize the animal in a new image.
According to international research, the machine learning market in 2025 exceeded 200 billion dollars, and the number of vacancies for ML specialists increased by 40% compared with the previous year. ML is becoming a mandatory skill for IT specialists and data analysts.
In Russia, machine learning is actively implemented in the banking sector, retail, industry and public administration. About how ML is used in fintech to combat fraud, read the article Fraud monitoring.
Main tasks of machine learning
1. Prediction (regression)
Predicting numeric values based on past experience. Example: analyzing apartment prices in your area to determine the sale price, forecasting demand for goods, predicting exchange rates.
2. Classification
Dividing objects into categories. Example: the filter in your email that distributes messages into the "Inbox" and "Spam" folders, handwritten digit recognition, determining a disease by symptoms.
3. Clustering
Grouping objects by hidden features without preliminary labels. Example: segmenting clients by behavior for personalized offers, analyzing social networks to identify communities.
4. Recommendations
Analyzing your preferences to show similar things. Example: playlists or films selected for you by services like Yandex Music or online cinemas.
Main approaches to learning
1. Supervised learning
Models are trained on labeled data, where there is a correct answer for each example. The algorithm learns to match input data with correct outputs. Used for classification and regression. Examples: spam recognition, price prediction, disease diagnostics.
2. Unsupervised learning
The algorithm looks for hidden connections and structures in unlabeled data. The main tasks are clustering (combining similar objects) and anomaly detection. Examples: client segmentation, detection of fraudulent transactions, social network analysis.
3. Reinforcement learning
The agent learns by trial and error, receiving "rewards" or "penalties" for its actions to maximize the final result. Often used in robotics, games (for example, AlphaGo), control of autonomous vehicles and process optimization.
About how these approaches are applied in business, read the article Fintech.
Main stages of developing an ML model
1. Data collection and preparation
This is the most important stage. The rule "garbage in — garbage out" is the gold standard for ML projects. Data must be cleaned of errors, missing values handled, and brought to a single format. The quality and volume of data directly affect the accuracy of the model.
2. Choosing an algorithm and training the model
Depending on the task, a suitable algorithm is selected: linear regression, decision trees, support vector machines, neural networks. The model is "trained" on historical data — optimal parameters are selected to minimize errors.
3. Quality assessment
The model is tested on new data that it did not see during training. This makes it possible to assess how well it generalizes knowledge and does not overfit (when the model simply memorizes answers but does not learn to generalize).
4. Deployment into production (MLOps)
The model is deployed in the real IT infrastructure — connected to applications, websites or internal systems. After deployment, the model requires constant monitoring and retraining on new data.
ML tools and technologies
Programming languages
- Python: The undisputed leader among programming languages for ML. It has a simple syntax and a huge number of libraries.
- R: Used for deep statistics and data analysis.
- C++: Used in production when maximum operating speed is important.
- Java: Used to integrate ML into large corporate systems.
Main Python libraries
- NumPy: For working with multidimensional arrays and mathematical operations.
- Pandas: For analysis and processing of tabular data.
- Scikit-learn: For classical machine learning algorithms.
- TensorFlow and PyTorch: For deep learning and neural networks.
- Matplotlib and Seaborn: For data visualization.
About how data encryption is used in ML systems, read the article Encryption.
The ML engineer profession
An ML engineer is a specialist who creates algorithms and trains artificial intelligence to find patterns in data, make decisions and automate tasks without direct human intervention. This is a profession at the intersection of classical programming, mathematics and business.
What an ML engineer needs to know
- Mathematics and statistics: linear algebra, probability theory, optimization methods.
- Programming: confident command of Python and its data analysis libraries.
- Development tools: SQL for working with databases, Git for version control, Docker for containerization.
- Cloud platforms: AWS, Google Cloud, Yandex Cloud for scaling solutions.
About how to become an ML engineer, read the article Low-code.
Where machine learning is applied
- Banking: credit risk assessment, scoring, detection of fraudulent transactions.
- Retail and e-commerce: recommendation systems, dynamic pricing, demand forecasting.
- Medicine: analysis of images (MRI, CT), diagnostics, drug development.
- IT services: voice assistants, autopilots, chatbots, smart translators.
- Industry: predictive maintenance of equipment, quality control.
About how ML helps protect business from threats, read the article Fraud monitoring.
Frequently asked questions
What is machine learning in simple words?
Machine learning is a way to teach a computer to find patterns in data without rigid programming. Instead of writing rules for every situation, you give the algorithm thousands of examples, and it learns from them itself. For example, you show it 10,000 photos of cats and dogs, and it learns to distinguish them. Read more about related technologies in the article Artificial intelligence.
How is AI different from machine learning?
Artificial intelligence is a broad concept of creating "smart" machines. Machine learning is a specific method of creating AI in which the computer learns from data. We can say that machine learning is a subfield of AI, its main tool. Not every AI uses machine learning, but almost all modern AI systems are based on ML. Read more about the differences in the article Artificial intelligence.
What are examples of machine learning in everyday life?
Examples of ML are everywhere: the spam filter in email, film recommendations on Kinopoisk, voice assistants (Alisa, Siri), face recognition in the phone, weather forecasts, product recommendations in online stores. Banks use ML to assess credit risks and detect fraudulent transactions. About how ML protects banks, read the article Fraud monitoring.
What language is machine learning written in?
The main language of machine learning is Python. Most neural networks are created in it thanks to a huge number of libraries (PyTorch, TensorFlow, Scikit-learn). R is also used (for statistics), C++ (for high performance) and Java (for corporate systems). For beginners, Python is the best choice thanks to its simplicity and huge community.
How much does an ML engineer earn?
The salary of an ML engineer in Russia: Junior — 80,000–150,000 rubles, Middle — 150,000–250,000 rubles, Senior — 300,000–500,000+ rubles per month. In the USA, salaries are significantly higher: Junior — $90,000–$110,000 per year, Senior — $150,000–$250,000+ per year. Income depends on experience, technology stack and industry. About the ML engineer profession, read the article Low-code.
Can I learn machine learning on my own?
Yes, it is quite possible to learn machine learning on your own. Start with learning Python, the basics of mathematics (linear algebra, probability theory) and libraries (NumPy, Pandas, Scikit-learn). Free courses are available on Stepik and Coursera. Practice on the Kaggle platform with real datasets. The full path from beginner to Junior takes 6–12 months. About where to start, read the article Low-code.
How is ML different from neural networks?
Machine learning is a broad class of methods for finding patterns in data. Neural networks are only one of the machine learning algorithms, inspired by the structure of the human brain. Any neural network is machine learning, but not all machine learning is neural networks. For example, decision trees and linear regression are also ML, but they are not neural networks. Read more about neural networks in the article Artificial intelligence.
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