What is AI and how to learn it?

What exactly is AI?

Artificial Intelligence (AI) is a branch of computer science whose goal is to build systems that can do tasks requiring “human intelligence”; tasks like learning from experience, reasoning, understanding natural language, recognizing images and making decisions. Simply put, AI means teaching machines to recognize patterns and produce a suitable response themselves, instead of running pre-written instructions.

The roots of this field go back to the historic Dartmouth conference in 1956; where the term “artificial intelligence” was used for the first time. But its real leap happened in the past decade with the availability of three key factors: abundant data, high processing power and advanced learning algorithms. This combination is what made today’s tools like large language models possible.

AI vs machine learning vs deep learning

These three terms are often used interchangeably, but their relationship is nested: deep learning is a subset of machine learning, and machine learning is a subset of AI. The table below clarifies the differences:

Simple summary: every deep learning is a kind of machine learning, and every machine learning is a kind of AI; but the reverse is not true. For a deeper understanding of how language models work, you can read the article how large language models work.

Types of AI

AI is usually divided into three categories based on the breadth of ability:

• Narrow AI: specialized in one specific task. All today’s tools — from translation to chatbots — are in this category.
• Artificial General Intelligence (AGI): a hypothetical system that, like a human, can learn and do any intellectual task. Not yet achieved.
• Superintelligence: a theoretical concept in which machine intelligence surpasses humans in all areas; for now mostly a topic of futures studies.

The important point is that all the tools you work with today are of the “narrow” type; that is, however impressive they are, they have no capability outside their trained domain.

Generative AI and large language models

The most important recent wave is Generative AI; systems that, instead of merely classifying data, produce new content — text, image, audio and code. At the heart of this wave are large language models (LLMs) like ChatGPT, Claude and Gemini.

These models are trained by processing massive amounts of text, and in the simplest description, what they do is “predict the most probable next word.” This seemingly simple mechanism, when run at a massive scale, leads to the ability to answer, summarize, translate and write code. For a closer look at these independent, decision-making tools, the article what are AI agents and the piece the MCP protocol and connecting models to tools are a good place to continue.

AI in business

For businesses, the real value of AI lies in “automating repetitive tasks” and “responding quickly to customers.” A few in-demand uses:

• Automated support: connecting a Telegram bot to AI for round-the-clock answers to frequent customer questions.
• Content creation: writing article drafts, captions and product descriptions with the help of language models.
• Data and sales analysis: classifying leads, predicting customer behavior and prioritizing orders.
• Personalization: suggesting the product or content suited to each user.

If you want to see how AI can help your business, see the Filtor AI services page, or use the AI readiness test to gauge where your business should start.

An AI learning roadmap

Contrary to common belief, learning AI does not necessarily start with heavy math. Your path depends on your goal: being a “user” is different from being a “builder.”

Prerequisites

If you intend to build models and become a machine learning engineer, you need these foundations: mathematics (linear algebra, probability, statistics and calculus basics), Python programming and data-driven thinking. But if you just want to use the tools, none of these is necessary at first.

A step-by-step path

1. Learn the basics of Python; the simplest and most widely used language in this field.
2. Practice working with data libraries like NumPy and Pandas.
3. Move on to machine learning basics (Andrew Ng’s famous course is a standard starting point).
4. Follow deep learning and neural networks with a project-based course.
5. Learn hands-on work with language models, APIs and prompt engineering.
6. Build a real project; nothing builds skill like building.

Credible learning resources

To start, we recommend these free, high-quality resources: the Elements of AI course for understanding the basics, the DeepLearning.AI platform and fast.ai for practical deep learning, and Kaggle Learn for practice with real data. For understanding trends and news in the field, Google AI is also a good source.

Prompt engineering; the practical starting point

If you are not a programmer or want fast results, prompt engineering is the best entry door. Prompt engineering is the art of writing precise, structured instructions for language models to get higher-quality output. This skill needs neither coding nor mathematics, but its effect on output quality is remarkable.

To master this skill, read the complete guide to the principles of prompt engineering, and if you want to know how it differs from more advanced approaches, the article prompt engineering vs loop engineering will help. To see all the material in this area, you can also visit the Filtor articles section.

Common mistakes in learning AI

• Jumping to advanced topics: starting with complex neural networks without learning the basics makes the path longer.
• Drowning in theory: endlessly watching courses without building projects does not create real skill.
• Ignoring the foundations: for the model-building path, neglecting math and statistics costs dearly in the long run.
• Quitting too early: the learning curve is steep at first; consistency matters more than speed.
• Relying on one tool: tools change quickly; fundamental concepts last.

Frequently asked questions