Courses on Neural Networks and Artificial Intelligence: Where to Study in 2026

Courses on neural networks and artificial intelligence currently fall into two different groups. Some teach you how to use neural networks at work: writing texts, making presentations, analyzing data, and automating routine tasks. Others go deeper: Python, machine learning, neural networks, ML Engineering, and production.

Below is a selection of current options for a Russian-speaking audience. I have divided the courses by level to make it easier to choose: a quick start without code, a career in AI, free academic programs, and advanced deep learning.

In Brief: What to Choose

If you need a quick start without code, look at courses on neural networks for work, marketing, study, and business.

If you want a career in AI, choose programs with Python, statistics, ML, projects, and a portfolio.

If you need a solid foundation for free, start with DLS MIPT, Stepik, or Open Education.

If your goal is deep learning, look at separate tracks on neural networks, CV, NLP, and recommender systems.

If you are studying for work, check not only the curriculum but also the practice: projects, a mentor, deadlines, and assignment reviews.

Quick Start with Neural Networks

Yandex Practicum: Catalog of Courses on Neural Networks

A good entry point if you need applied AI without a long dive into mathematics. The catalog includes courses for work, business, neural network production, and Python with neural networks. Suitable for those who want to learn through assignments and immediately build work-ready scenarios.

Link: Yandex Practicum course catalog

Netology: Neural Networks for Beginners

A course on everyday work with ChatGPT, YandexGPT, DeepSeek, GigaChat, and generative services. Suitable for marketers, managers, writers, teachers, and anyone who needs a confident start without programming.

Link: Netology course

Netology: How to Use Neural Networks

A short free option to try the topic without a large budget. It is convenient to take before buying a long course: it will become clearer which tasks you actually want to solve with AI tools.

Link: free Netology course

Skillbox: Neural Networks: A Practical Course

An applied course for tasks involving text, images, presentations, analysis, and automation. Its strong point is a clear package for beginners and a focus on work scenarios, not just theory.

Link: Skillbox course

MIPT: Neural Networks for Work

A calm applied track on using AI in office, analytical, and creative tasks. Suitable for specialists who want to strengthen their current work but are not planning to immediately become ML developers.

Link: MIPT course

OTUS: Artificial Intelligence. Basic

An option for those who want to understand the basic concepts of AI, models, and practical scenarios before choosing a long program. Useful if the topic still feels foggy and you want to bring some order to it.

Link: OTUS course

Profession: ML, Data Science, and AI Engineering

Karpov.Courses: ML Engineer

A track for those who are ready for code, experiments, metrics, and engineering work with models. Best considered if you already have a foundation in Python or analytics and want to move toward ML engineering.

Link: Karpov.Courses course

Karpov.Courses: Start in Machine Learning

A gentle entry point before a large ML track. Suitable if Python and statistics are already familiar at a basic level, but you lack a system: how models, training, quality, and practice on tasks work.

Link: Start in Machine Learning course

SkillFactory: Machine Learning Engineer

A career-oriented program about creating, training, optimizing, and deploying ML models. This is no longer a course about prompts, but training for those who want to work with data and models as an engineer.

Link: SkillFactory course

Yandex Practicum: Data Scientist

A broad path into Data Science: Python, SQL, statistics, machine learning, and projects. A good option if you need a foundation for the profession, not just a set of techniques for generative neural networks.

Link: Data Scientist course

Skillbox: Artificial Intelligence Engineer

A long commercial track for those who want to move into developing AI systems. When choosing, look at the composition of projects, workload, mathematics requirements, and how the course gets you to a portfolio.

Link: Skillbox course

GeekBrains: Artificial Intelligence Specialist

Another path into the profession, where programming, ML, and practical projects matter. It is worth comparing with other long programs in terms of duration, mentorship, workload, and career support.

Link: GeekBrains course

Free and Academic Options

DLS MIPT: Deep Learning Courses

One of the strongest free starting points for those who want to understand neural networks more deeply. There are different levels, a lot of mathematics and practice, so it is a good choice for future ML specialists.

Link: DLS MIPT courses

Stepik: Neural Networks and Computer Vision

A classic Russian-language course for getting started with neural networks and computer vision. Suitable for those who want to study at their own pace and are not afraid of tasks, code, and independent practice.

Link: course on Stepik

Open Education: Machine Learning

An academic option if you need a university-level foundation in machine learning. Better suited for those who are ready to read, calculate, understand algorithms, and are not expecting an entertainment format.

Link: course on Open Education

Open Education: Applied Machine Learning in Python

A practical course for those who want to connect Python and ML tasks. A good additional option alongside a longer program or an independent learning path.

Link: ITMO course

Deep Learning and Advanced Tracks

Netology: Deep Learning

A course for those who are already ready to go deeper into neural networks: computer vision, text processing, recommender systems, and modern models. It is better to choose it after basic Python and ML.

Link: Deep Learning course

ProductStar: Deep Learning: Artificial Intelligence

A commercial track for studying deep learning in an applied format. Before buying, it is worth comparing the program with DLS and Netology: the depth of mathematics, projects, feedback, and the final portfolio.

Link: ProductStar course

How to Avoid Choosing the Wrong Course

Do not buy a long course just because of the word “neural networks”. First decide whether you need prompting for work or a career in ML.

Check the entry level. Prompting usually does not require code; ML almost always requires Python, mathematics, and patience.

Look at the projects. A good course should provide a clear outcome: a portfolio, work scenarios, models, notebooks, or automations.

Compare support. A mentor, assignment reviews, and deadlines are often more important than beautiful promises on a landing page.

Check how up to date the program is. Tools change quickly in AI: ChatGPT, Gemini, Claude, DeepSeek, YandexGPT, GigaChat, Runway, Suno, and other services should be regularly updated in the materials.

FAQ

Which course should I start with if I am not a programmer?

Start with a short no-code course on neural networks: Yandex Practicum, Netology, Skillbox, or MIPT. There it is easier to understand which tasks AI can actually handle at work: text, presentations, spreadsheets, ideas, images, search, and automation.

Do I need mathematics to study artificial intelligence?

For applied use of neural networks — no. For a career in ML, Data Science, and AI Engineering — yes: you will need statistics, linear algebra, metrics, optimization, and an understanding of algorithms. That is why you should not mix up prompt courses and professional ML programs.

Can I study AI for free?

Yes. DLS MIPT, Stepik, and Open Education are good for building a foundation. The free path requires more self-discipline: no one will lead you by the hand, but the quality of the materials can be very high.

Which course should I choose for changing careers?

Do not look at the loudest brand, but at the program and the outcome. For a career change, you need Python, SQL, ML, projects, reviews, a portfolio, and a realistic workload. If you have little time, start with a short introductory course and only then take a long professional program.

What is more important: a certificate or a portfolio?

A portfolio is more important. A certificate helps show that you completed training, but an employer or client needs examples: models, notebooks, automations, data cases, bots, analytical projects, or work-ready AI scenarios.