English for Learning ML

Of course — and your English is already good enough to ask a clear, well-structured question, so you're further along than you might think. Since your real goal is learning ML/DL, I'll focus on the kind of English that actually helps with that: reading papers and docs, understanding lecture videos, and writing/speaking about technical topics. That's a bit different from general English, and it's actually easier to target.

The key mindset ​

For your situation, don't treat "learn English" and "learn ML" as two separate tasks. Combine them. Most of the ML resources I recommended earlier are in English, so studying them is English practice. Every time you watch Andrew Ng or read the Mathematics for Machine Learning book, you're learning both at once. This is the single most efficient thing you can do, because the vocabulary you learn is exactly the vocabulary you need.

A practical method for each skill ​

Listening. This is usually the biggest gap for technical learners, and ML lectures are perfect practice. Watch English ML videos (3Blue1Brown, StatQuest, Andrew Ng) with English subtitles on — not subtitles in your own language. Reading and hearing at the same time trains your ear to connect sounds to words. Once a topic feels comfortable, rewatch it with subtitles off. StatQuest is especially good here because Josh Starmer speaks slowly and clearly.

Reading. Start with documentation and tutorials (they use simple, repetitive language), then move up to blog posts, then to research papers, which are the hardest. When you read papers, don't look up every unknown word — only look up words that repeat, because those are the ones that matter. A tool like an in-browser dictionary (for example, the "Google Dictionary" browser extension, where you double-click a word to see its meaning) makes this fast and painless.

Vocabulary. Use a spaced-repetition flashcard app — Anki is the standard, and it's free. But instead of memorizing random word lists, make cards from words you actually meet while studying ML. When you hit a new term ("gradient," "regularization," "convergence"), make a card with the word, its meaning, and the real sentence you found it in. This way you only learn useful words, and the example sentence helps you remember how to use it.

Speaking and writing. This is where most self-learners get stuck because it feels scary. A low-pressure way to start: explain an ML concept out loud to yourself in English, as if teaching someone. This is called the "Feynman technique," and it builds both your English and your understanding at the same time. For writing, try writing short summaries of what you learned, or participate in English ML communities (Reddit's r/MachineLearning, or discussion forums on Coursera). You can also practice conversation with me — ask me to correct your English, or to explain things in simpler words.

Good free resources ​

For general English foundations, if you feel your grammar or everyday vocabulary needs work: BBC Learning English (free website and YouTube channel, very high quality) and Duolingo (good for daily habit and basics, though limited on its own). For listening practice with transcripts, most TED Talks have full transcripts you can read along with, and many are about science and technology.

For pronunciation, YouGlish is a great free tool: you type a word and it shows you real videos of people saying it, so you can hear how a term like "eigenvalue" is actually pronounced.

For grammar and writing help while you write, Grammarly (free version) catches mistakes and, over time, teaches you your common error patterns.

A simple weekly routine ​

You could combine everything like this: study your ML material in English as your main input, keep an Anki deck of new technical words you meet, watch one ML video with subtitles as focused listening practice, and once a week write a short English summary of what you learned (and you can paste it to me for corrections). This way you're improving your English every day without adding a separate "English study" burden on top of your ML learning.

One encouraging note: technical English is actually more forgiving than everyday English. The vocabulary is limited and repetitive, the grammar in papers is fairly formulaic, and precision matters more than sounding natural. Many excellent ML researchers are non-native speakers. You do not need perfect English to succeed — you need enough English, and you already have a solid start.

Would you like me to help you set up a specific study plan, or would it help if I explain ML concepts to you in simpler English as you learn? I'm happy to adjust how I write to match your level.