wtf is machinelearningroadmap?

This repository is a detailed learning roadmap for machine learning, written in Russian and aimed at the Russian-speaking ML community. It solves the problem of not knowing where to start or how to structure your learning journey when entering the field of machine learning, deep learning, and AI. The roadmap is organized into seven sequential tracks covering progressively advanced topics. It starts with Python fundamentals and math, moves into classical machine learning techniques, then deep learning with neural networks, followed by large language models and transformer architectures, generative AI, putting models into production (called MLOps), and finally specialty areas you can choose to focus on. Each track comes with an estimated time commitment and a recommended artifact to build, such as a notebook or project. The guide also includes honest warnings about common misconceptions in the field, like promises of learning machine learning in three months, and explains which types of roles actually exist in practice. It also provides differentiated starting points based on your background, whether you are a working developer, a student, or a scientist from another field. You would use this roadmap if you are a Russian-speaking person who wants to enter the machine learning field and needs a realistic, structured plan rather than a random collection of courses. The roadmap covers tools like Python and PyTorch and concepts like RAG, fine-tuning, and AI agents, but only teaches them when contextually appropriate in the progression.