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A mid-range Android phone gets you through the foundations, including most of Python, using Google Colab in a browser. From subject 03 onwards a laptop makes life much easier — mainly because of the keyboard and the screen, not the processing power, since the heavy work happens on Colab's servers. If a laptop is the only thing stopping you, write to us; we keep a small equipment list.

Some, yes — the courses are taught in English, and so is every piece of documentation and research in this field. But the English you need is technical rather than conversational, which is a far smaller target than it sounds: a few hundred recurring words, plus the confidence to read a manual slowly. We keep the language plain, define every term once, and never use an idiom where a plain verb will do.

Less than people fear, and more than the marketing suggests. Subject 02 exists precisely for this: it covers the linear algebra, calculus and probability that actually appear inside models, and it names what you can skip. For machine learning you need comfort with algebra and an idea of what a derivative means. For deep learning you need to be genuinely at ease with matrix multiplication and the chain rule.

We will not promise a number, because it depends on your starting point and how many hours a week you have. What we can say: the people who reach paid work are the ones who finish projects and publish them, not the ones who watch the most lessons. Two finished projects on GitHub with a clear write-up is worth more to an employer than ten certificates.

We will issue a completion record for each subject, and we are honest about its weight: it is not accredited and no employer knows our name yet. Your project portfolio is the credential that works. We would rather help you build that than sell you a PDF.

Yes, and it is a priority. All courses can be followed entirely at home with no attendance requirement. Some study groups are women-only with women facilitators. In any group you may use a first name only, and no photograph or camera is ever required. If a group does not feel safe, tell a facilitator or write to us directly and we will act.

Thank you — please tell us. Open an issue on GitHub if you are comfortable with that, or send an email. Corrections are credited in the lesson notes, and finding a real error in the curriculum is one of the best first contributions you can make.