🗺 Guide to the World of Machine Learning.

Table of Contents:
🔛 Where to Start Your ML Journey

🔑 Kaggle - The Best ML Training Ground

🤓 Mathematics Under the Hood

🧠 From Classics to Neural Networks

🤯 How LLMs Work Under the Hood

⚙️ Playing with Models: Groq, OpenRouter, and Others

🤗 Hugging Face - A Treasure Trove of Models

💬 The Power of Community

🧰 How to Build Your First AI Assistant Today?

🕶 Fine-tuning and RAG: Do We Need to Retrain Models?

💻 Where to Run Models If You Don't Have a Powerful GPU at Home?

🧑‍🎓 GPT Week from Yandex: How GPT Is Trained and Fine-tuned
...

Guide to the World of Machine Learning is a series of posts about how to get started in Machine Learning.

A kind of "basic minimum" that you can start with on your own to build a foundation.

The roadmap does not claim to be exhaustive.

Its goal is rather to narrow down the vast space of materials and information to an effective minimum, an essence.

The focus will be slightly more shifted towards working with large language models - LLMs.

The guide can be useful if you want to start working with GPT models / agents while understanding what is happening under the hood.

Also, if you just decided to open up machine learning a bit for yourself.

👩‍💻 Data Flow