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Channel

Machinelearning

@ai_machinelearning_big_data

On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Stars · Advertising · Posts · Posts edited after publishing · Posts recorded as deleted · Citations · Telegram's recommendations · Cite this entry

275,659subscribers

-11,740 since we began measuring on 5 August 2026

Risers and fallers across the register · movement among entries of 100,000–316,228.

Register entry

Telegram ID-1001114591086
TypeChannel
Username@ai_machinelearning_big_data
DescriptionПогружаемся в машинное обучение и Data Science Показываем как запускать любые LLm на пальцах. По всем вопросам - @haarrp @itchannels_telegram -🔥best channels Реестр РКН: clck.ru/3Fmqri
Created15 March 2017 — measured — cross-checked against a third-party dataset (ext.tg_channel)
First recorded6 August 2026
Last confirmed live10 November 2026
Measurements held65
Confirmed unchanged1 time, most recently 10 November 2026
On Telegramt.me/ai_machinelearning_big_data

Topic

Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 8 August 2026 and assigned it the closest of 31 fixed categories, at 100% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and the other topics.

Growth

275,659287,399281,5295 August 202610 November 2026
65 measurements spanning 97 days, net -11,740. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 273,898–289,160 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 65
Measured (UTC)SubscribersChange
10 Nov 2026, 14:49275,659-184
9 Nov 2026, 00:42275,843-183
7 Nov 2026, 18:59276,026-174
6 Nov 2026, 16:47276,200-142
5 Nov 2026, 13:26276,342-170
4 Nov 2026, 12:24276,512-146
3 Nov 2026, 12:39276,658-62
2 Nov 2026, 14:24276,720-108
1 Nov 2026, 11:39276,828-131
31 Oct 2026, 12:00276,959-111
30 Oct 2026, 13:14277,070-120
29 Oct 2026, 15:10277,190-81
28 Oct 2026, 12:06277,271-115
27 Oct 2026, 13:14277,386-107
26 Oct 2026, 14:09277,493-62
25 Oct 2026, 13:46277,555-34
24 Oct 2026, 12:41277,589-73
23 Oct 2026, 11:09277,662-71
22 Oct 2026, 11:55277,733+69
21 Oct 2026, 02:10277,664first reading

Engagement

523 posts held, back to 3 August 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 202 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
7.32%
avg views ÷ 275,659 subscribers
Avg views / post
20,200
171 posts measured
Reaction rate
1.49%
reactions ÷ views · ER floor
Posts in window
171
of 523 held

ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.

ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.

What these figures were computed from
WindowRolling 30 days · latest post in window 10 November 2026
Posts held523 (3 August 2026 – 10 November 2026)
Views total3,452,470
Reactions total51,486
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken10 Nov 2026, 19:43 UTC

Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.

Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.

Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.

What this channel posts

Photos
≈5,670
Videos
≈1,340
Links
≈6,030

Lifetime counters from Telegram’s own channel header, read 10 November 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.

Video runtime
6h 17m
Average length
2m 42s

Measured directly from 140 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.

Reaction mix

111,342 reactions across 518 posts, in 60 distinct kinds. The most used accounts for 24.5% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
👍27,31724.5%
❤14,49013.0%
🔥13,85612.4%
🤔11,89110.7%
👏8,3527.50%
😁7,2976.55%
🎉5,5825.01%
💯4,1853.76%
🤣2,9682.67%
🤩2,8222.53%
🤓1,9431.75%
👨‍💻1,7881.61%
⚡1,7681.59%
🤬1,0340.929%
🤨7630.685%
👌6340.569%
😢4560.41%
🥰3740.336%
🥱3350.301%
😐3300.296%
40 further kinds3,1572.84%

No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.

Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.

Coverage. Reactions were read on 523 of the 523 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 112,060 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 523 most recent posts we hold, published 3 August 2026 to 10 November 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.

Telegram Stars

Stars received
103
across the posts below
Posts paid on
58
of 523 we hold a reading for · 11%
Most on one post
11
single highest reading

A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @ai_machinelearning_big_data. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.

Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.

This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.

Counted over the 523 most recent posts we hold for this entry, published 3 August 2026 to 10 November 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.

Advertising

Ad load
3.82%
20 of 523 posts carry an ad marker
Regulatory tokens
20
posts carrying an erid · 20 distinct tokens
Median views · ads
17,800
over 20 measured posts
Median views · rest
19,600
over 503 measured posts

An ad marker, not a judgement about a post. A post is counted here because it carries one of two explicit markings: an erid token, which Russian law has required on paid placements since 2022 and which is issued against a specific advertising contract, or a #реклама / #ad hashtag in the body, which is the channel declaring it itself. The first is documentary; the second is a self-declaration and is weaker. No classifier reads the text and decides — nothing on this site guesses that a post is an advertisement.

This is a floor, and it can only ever be a floor. A channel that runs paid placements without marking them produces no marker for us to count, and an unmarked ad is indistinguishable from an ordinary post on the public surface. The ad load above therefore means “the share of posts that declared themselves”, never “the share of posts that were paid for”. A low figure is not evidence of a channel that runs few ads.

Both figures are medians, and no ratio between them is published. Each is a view reading that actually occurred on a post, picked by percentile_disc rather than averaged, so one viral post cannot move it and no interpolated value is invented between two readings. They are printed side by side with the count behind each rather than as a ratio: an ad and an ordinary post are not otherwise matched — for topic, for length, for hour of day — so the gap between them is a description of two groups and not the effect of one being an ad.

Advertising tokens recorded on this entry
eridPostsFirst seenLast seen
2SDnjd36qgF111 August 202611 August 2026
2SDnjf3hPDV130 September 202630 September 2026
2VSb5ybG7Cv123 September 202623 September 2026
2VfnxxSVyCQ130 September 202630 September 2026
2Vtzqv48qtp114 September 202614 September 2026
2VtzqvGyx6w14 September 20264 September 2026
2VtzqvMUFnd127 October 202627 October 2026
2Vtzqw727Ws127 August 202627 August 2026
2VtzqwVG8zh121 October 202621 October 2026
2Vtzqwno39C17 August 20267 August 2026
2VtzqxJgkdk131 October 202631 October 2026
2VtzqxP6jLi15 November 20265 November 2026

A token repeated across several posts is one advertising contract placed more than once, which is what the identifier is for. The strings are reproduced exactly as they appeared in the post or in its click-through URL and are not validated against any registry — we record the marker a channel published, and whether it resolves to a real contract is a question for the register that issued it. 8 further tokens are held for this entry and not listed here.

Measured over the 523 most recent posts we hold, published 3 August 2026 to 10 November 2026. Views are the latest single reading held for each post, and any reading at or above 1,000 is rounded by Telegram to three significant figures.

Recent posts

Telegram’s own ‘edited’ marker: 23 of 171 posts held from the last 30 days (13.5%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).

10 Nov 2026, 16:59 UTC≈9,370 views46 reactionsread 10 November 2026
Photo

✔️ Google выпустила Gemini 4 Carbon. Новая модель лидирует в трёх агентных бенчмарках Gemini 4 Carbon ориентирована на программирование, ИИ-агентов и научные задачи. Модель заняла первые места в CursorBench 4.0, Perplexity WANDR и Zapier AutomationBench. Стоимость выполнения задач более чем вдвое ниже, чем у ближайшего конкурента. Google также представила /swarm для Antigravity. Теперь несколько ИИ-агентов могут п…

❤20👍10😁10🔥5🥰1

10 Nov 2026, 11:00 UTC≈14,700 views84 reactionsread 10 November 2026
Video

✔️ На ChatGPT приходится 81,4% мирового трафика ИИ-ассистентов Такую оценку даёт исследование агентства Graphite, в котором доля Claude составляет 6,8%, Google Gemini 5%, DeepSeek 4%, Grok 1,9% и Perplexity 0,9%. После семи месяцев без роста аудитория ChatGPT прибавила почти 20% и в сентябре достигла рекордных 61,35 млрд визитов, при этом общая посещаемость ИИ-сервисов за год выросла на 37% и превысила 76 млрд визи…

👍60❤15🔥7🥰2

10 Nov 2026, 09:21 UTC≈14,300 views139 reactionsread 10 November 2026
Photo

✔️ Meta* выпустила компактную модель Muse Ember Новинка спроектирована для циклических агентских задач - вызова инструментов, парсинга объемных документов, маршрутизации и написания фрагментов кода. По бенчмаркам компании, Muse Ember обходит Claude Haiku 5.5 и GPT-6 Luna по соотношению цены и интеллекта, выдавая рекордные результаты в своей весовой категории. Модель поддерживает контекстное окно на 1 млн токенов и…

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10 Nov 2026, 09:10 UTC≈15,700 views198 reactionsread 10 November 2026
Photo

🤑 Anthropic заработала до $2,33 млрд на клиентах, которых потом заблокировала Anthropic могла получить от $1,66 млрд до $2,33 млрд от клиентов, чьё использование Claude впоследствии признали нарушением правил компании. В обновлённом проспекте IPO, поданном в SEC 2 ноября 2026 года, компания сообщила, что на таких клиентов пришлось около 5-7% выручки за первые девять месяцев 2026 года. При общей выручке $33,242 млр…

🤬73🤔38👍24❤19🤣17🤨10😁10🔥5

10 Nov 2026, 07:16 UTC≈15,800 views226 reactionsread 10 November 2026
Photo

🌟 Nous Research дообучила Qwen3.8-27B уходить от официальной позиции КНР в ответах Независимая лаборатория выложила Arete-27B, версию модели Qwen3.8-27B от Alibaba, дообученную отвечать на политически чувствительные для Китая вопросы документальными фактами вместо официальной линии КПК. Целью проекта Nous Research называет желание укрепить западные ценности в модели, обученной в Китае. Qwen3.8-27B выбрали как саму…

😁76👍67❤18🤔18👏16🌚8🔥7🤬6

9 Nov 2026, 19:01 UTC≈21,800 views225 reactionsread 10 November 2026
Photo

✔️ Anthropic представила Claude Fable 5.2 и Mythos 5.2: 1000 ИИ-агентов формализовали доказательство теоремы Ферма за 30 часов Новые модели получили серьёзные улучшения в программировании, математике, научных исследованиях и кибербезопасности. Что удалось сделать: - Математика: 1000 агентов формализовали доказательство Великой теоремы Ферма на Lean за 30 часов. Предыдущей исследовательской версии Claude потребовал…

🔥95💯58❤23👏22🎉12👍6🤩3🥱2

9 Nov 2026, 18:47 UTC≈16,900 views254 reactionsread 10 November 2026
Photo

📚 Оксфорд выпустил 180-страничный учебник по тому, как заглянуть внутрь нейросетей и понять, как они думают Исследователи University of Oxford и других научных организаций опубликовали масштабный обзор механистической интерпретируемости (Mechanistic Interpretability) и её применения для безопасности ИИ. Что внутри: - Superposition и Transformer Circuits: как нейросети кодируют информацию и выполняют вычисления. - …

❤94🤓79👍40👏15🤔10🔥8😍6🐳2

9 Nov 2026, 16:36 UTC≈16,000 views252 reactionsread 10 November 2026
Video

Модель на 800 млн параметров обошла Qwen 9B и Gemma 4 в генерации Linux-команд На Hugging Face появился dale-0.8b-GGUF - специализированная модель, которая превращает обычные текстовые запросы в команды Bash для Linux. Что умеет: Пишешь обычным языком: «Найди все большие файлы в текущей директории». Модель возвращает одну Bash-команду в структурированном JSON с кратким объяснением. Она также умеет отказываться о…

👍143👏30⚡23🤓22👨‍💻15❤13🔥6

9 Nov 2026, 15:25 UTC≈17,300 views116 reactionsread 10 November 2026
Photo

✔️Новость о математических рукописях OpenAI продолжает обсуждаться в математическом сообществе. Тут, например, подробно разобрали реакцию Теренса Тао, одного из самых влиятельных учёных современности. В лекции Math 2.0 он предложил смотреть не только на финальные ответы моделей, но и на то, как меняется сам процесс математической работы. По мнению Тао узким местом всё ещё остаётся проверка: что именно доказано, как…

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9 Nov 2026, 14:31 UTC≈17,300 views129 reactionsread 10 November 2026
Video

🖥️ Google представила TPU 8i Max и 8tx: новые ускорители для ИИ-агентов Google расширила линейку TPU восьмого поколения до четырёх систем. TPU 8i Max рассчитан на огромные контекстные окна и сложные агентные задачи: - Два объединённых TPU 8i. - В 2 раза больше вычислительной мощности. - В 2 раза больше SRAM и пропускной способности HBM. TPU 8tx предназначен для конфиденциальных ИИ-вычислений: - Аппаратная защита д…

👍75❤15🤩15🔥10👏8🎉5🆒1

9 Nov 2026, 13:45 UTC≈18,400 views218 reactionsread 10 November 2026
Video

🛰 99,999% мировых вычислений могут переехать в космос. Ларри Пейдж уже назвал эту цифру 🤯 Сооснователь Google, по словам инвестора Патрика О'Шонесси, считает, что практически все вычислительные мощности человечества в будущем окажутся на орбите. О'Шонесси рассказал об этом в подкасте Invest Like the Best. По его словам, кто-то спросил Пейджа, какая доля вычислений будет выполняться в космосе. Ответ: five nines, то …

👍79🤩49🤣46❤13🤓10👏7🔥6😁5

9 Nov 2026, 12:38 UTC≈18,200 views85 reactions1 Starread 10 November 2026
Photo

AI Analytics Week от Школы анализа данных Яндекса: встроить ИИ в аналитику С 23 по 27 ноября ШАД проведёт бесплатный онлайн-интенсив для аналитиков, ML-инженеров и специалистов по Data Science. На реальных кейсах Яндекса покажут весь путь — от бизнес-гипотезы до агентной системы, которая связывает отдельные решения в единый пайплайн. Что в программе: — Очистка данных с LLM и RAG-пайплайны — LLM в экспериментах: ра…

👨‍💻41👍14👏13❤5🤔4🤣4⚡3🥰1

Showing the 12 most recent of 523 posts we hold for @ai_machinelearning_big_data. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.

Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.

Posts edited after publishing

@ai_machinelearning_big_data edited 3 posts after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.

An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.

First edit seen
30 September 2026
Most recent edit
4 November 2026

Posts recorded as deleted after publishing

We recorded 7 posts on @ai_machinelearning_big_data as deleted — posts this register held that were missing from the channel’s public page on two separate reads, taken from different network addresses at least an hour apart.

A deletion is not wrongdoing. This register can see that a post it held is no longer on the channel’s public page, and when — not why. Expired ad placements, reposts, rotating links and corrections all look the same. This is a count of recorded detections, not an exact total: it misses deletions we could not see, because only a post we held and later re-read inside the page can be seen to go, and it may include a small number of wrong records. How this is measured.

First seen missing
11 August 2026
Most recent seen missing
29 October 2026
Median time before seen missing
32 hours
Seen missing in the last 30 days
3

Forward network

Republished by

Channels on the register that have forwarded this channel's posts into their own feed.

Republished by 101 registered channels. The 48 listed are the ones that have forwarded the most posts; the rest are counted here but not each listed.

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Alex Muzyka - Блог
@alexeymuzykablog · 1,940
2 posts
4-10-02
@borisyarovoy · 7
2 posts
Github
@github_code · 2,548
2 posts
4 ПРОМЫШЛЕННАЯ РЕВОЛЮЦИЯ
@klaus_shwab · 70
2 posts
Мобильная разработка
@mobdevelop · 3,841
2 posts
Morozov ML
@mrzvML · 7
2 posts
Python/ django
@pythonl · 58,287
2 posts
TensorFlow
@tensorflowblog · 1,310
2 posts
White Tensor [AI, ML, DL]
@white_tensor · 161
2 posts
AI Surfer Технологии
@AISurferRu · 73
1 post
Гайд по нейронкам
@ai4busines · 2
1 post
Generative AI
@ai_generative · 2,129
1 post
AI.Sorceress @ Cloud
@ai_sorceress · 251
1 post
MLOps notes
@ai_watchtower · 194
1 post
AISec [x\x feed]🍓🍌🍆
@aisecnews · 1,538
1 post

Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.

Mentions

Named by 147 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it. The 24 listed below are the most frequent namers; the rest are counted above but not each listed.

Named by

Channels on the register whose posts name this channel's handle.

Data Science. SQL hub
@sqlhub · 36,025
86 posts
Kali Linux
@linuxkalii · 55,842
69 posts
Data Science by ODS.ai 🦜
@opendatascience · 38,029
58 posts
Python вопросы с собеседований
@python_job_interview · 24,906
58 posts
Искусственный интеллект. Высокие технологии
@vistehno · 70,139
49 posts
ИИ — svodka.ai: краткие сводки каналов
@svodkaai_ai · 9,299
44 posts
Математика Дата саентиста
@data_math · 14,419
37 posts
Machine learning Interview
@machinelearning_interview · 30,560
37 posts
Golang
@Golang_google · 40,453
33 posts
Python/ django
@pythonl · 58,287
29 posts
Linux Education
@linux_education · 11,037
28 posts
Анализ данных (Data analysis)
@data_analysis_ml · 51,923
26 posts
Artificial Intelligence
@ArtificialIntelligencedl · 16,815
23 posts
cortex_pulse AI
@cortex_pulse · 41
20 posts
MEECH
@meechale · 2
20 posts
Библиотека баз данных
@sql_lib · 10,104
20 posts
Big Data AI
@bigdatai · 18,329
19 posts
SQL и Анализ данных
@databases_tg · 12,502
19 posts
Машинное обучение RU
@machinelearning_ru · 18,339
18 posts
Квест Теория Каст и Ролей
@alexkrol · 18,735
17 posts
DevOps
@DevOPSitsec · 23,892
16 posts
Технозаметки Малышева
@tsingular · 13,281
16 posts
Машиннное обучение | Наука о данных Библиотека
@machinelearning_books · 16,955
15 posts
Data Science
@datascienceiot · 42,496
14 posts

A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.

Channels Telegram recommends alongside this one

Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.

эйай ньюз
@ai_newz · 98,262
#1
Data Secrets
@data_secrets · 96,035
#2
Сиолошная
@seeallochnaya · 81,254
#3
Анализ данных (Data analysis)
@data_analysis_ml · 51,923
#4
Denis Sexy IT 🤖
@denissexy · 140,753
#5
Machine learning Interview
@machinelearning_interview · 30,560
#6
DeepSchool
@deep_school · 10,723
#7
LLM под капотом
@llm_under_hood · 30,219
#8
Dealer.AI
@dealerAI · 17,113
#9
Yandex for ML
@yandexforml · 19,136
#10
Машинное обучение RU
@machinelearning_ru · 18,339
#11
Градиент обреченный
@doomgrad · 8,840
#12
Институт AIRI
@airi_research_institute · 13,977
#13
Математика Дата саентиста
@data_math · 14,419
#14
Метаверсище и ИИще
@cgevent · 53,591
#15
Complete AI
@complete_ai · 7,887
#16
Data Science
@datascienceiot · 42,496
#17
Искусственный интеллект. Высокие технологии
@vistehno · 70,139
#18
Нескучный Data Science
@not_boring_ds · 12,081
#19
Python/ django
@pythonl · 58,287
#20
Жёлтый AI
@zheltyi_ai · 9,371
#21
New Yorko Times
@new_yorko_times · 10,720
#22
Машиннное обучение | Наука о данных Библиотека
@machinelearning_books · 16,955
#23
Data Science Jobs
@datascienceml_jobs · 21,642
#24
Derp Learning
@derplearning · 13,992
#25
Dev & ML Connectable Jobs
@dev_connectablejobs · 29,084
#26
🏆 AIeron | Data Feeling
@datafeeling · 14,208
#27
Data Science by ODS.ai 🦜
@opendatascience · 38,029
#28
Агенты ИИ | AGI_and_RL
@AGI_and_RL · 6,280
#29
Data Science. SQL hub
@sqlhub · 36,025
#30
Data Science Jobs
@datasciencejobs · 22,587
#31
AI для Всех
@nn_for_science · 15,453
#32
Golang
@Golang_google · 40,453
#33
Библиотека дата-сайентиста | Data Science, Machine learning, анализ данных, машинное обучение
@dsproglib · 18,290
#34
Data Secrets | Карьера
@data_secrets_career · 7,395
#35
AbstractDL
@abstractDL · 18,849
#36
Big Data AI
@bigdatai · 18,329
#37
Дратути Антон
@blog_toxa · 4,357
#38
Data Science | Machinelearning [ru]
@devsp · 19,659
#39
grokaem себя
@grokaem_seby · 2,759
#40
Data, Stories and Languages
@datastorieslanguages · 3,922
#41
Python вопросы с собеседований
@python_job_interview · 24,906
#42
КПД
@quant_prune_distill · 3,510
#43
DevOps
@DevOPSitsec · 23,892
#44
Душный NLP
@stuffyNLP · 6,718
#45
Artificial Intelligence
@ArtificialIntelligencedl · 16,815
#46
Knowledge Accumulator
@knowledge_accumulator · 5,683
#47
Mashkka про Data Science
@mashkka_ds · 5,305
#48
Start Career in DS
@start_ds · 11,463
#49
РИСЕРЧОШНАЯ
@researchoshnaya · 2,464
#50
Пресидский залив
@neural_prosecco · 8,653
#51
XOR
@xor_journal · 195,549
#52
gonzo-обзоры ML статей
@gonzo_ML · 24,695
#53
Kali Linux
@linuxkalii · 55,842
#54
То шо нейросети
@toshoseti · 1,390
#55
Young&&Yandex
@Young_and_Yandex · 112,989
#56
epsilon correct
@epsiloncorrect · 8,199
#57
ФКН: Вакансии
@vacancy_cs · 18,429
#58
Хабр
@habr_com · 134,497
#59
Нейроканал
@neuro_channel · 12,073
#60
🏄 Соревновательный Data Science | Kaggle | Чемпионаты
@data_science_winners · 3,366
#61
ML for Value / Ваня Максимов
@ml4value · 5,676
#62
Information Retriever
@inforetriever · 4,167
#63
Время Валеры
@cryptovalerii · 32,082
#64
Техножрица 👩‍💻👩‍🏫👩‍🔧
@tech_priestess · 15,110
#65
Старший Авгур
@senior_augur · 8,372
#66
ODS #jobs
@odsjobs · 14,582
#67
Kali Novskaya
@rybolos_channel · 19,136
#68
еба́ные идеи для резерча
@ebaresearch · 5,370
#69
Борис опять
@boris_again · 17,963
#70
Neural Shit
@NeuralShit · 54,643
#71
Just links
@j_links · 6,943
#72
Поступашки - ШАД, Стажировки и Магистратура
@postypashki_old · 47,200
#73
Заметки Computer Vision инженера
@CVML_team · 6,118
#74
Сергей Марков: машинное обучение, искусство и шитпостинг
@oulenspiegel_channel · 12,374
#75
addmeto
@addmeto · 72,001
#76
DLStories
@dl_stories · 15,546
#77
Physics.Math.Code
@physics_lib · 146,676
#78
Kantor.AI
@kantor_ai · 12,215
#79
Data jobs — вакансии по data science, анализу данных, аналитике, искусственному интеллекту
@datajob · 15,139
#80
Take Friends to Luna Park
@hrlunapark · 21,006
#81
kyrillic
@kyrillic · 61,235
#82
Эксплойт
@exploitex · 2,022,997
#83
R77 AI | Кейсы в ИИ (от выпускников МФТИ)
@r77_ai · 7,511
#84
Бэкдор
@whackdoor · 1,538,229
#85
Нескучный Data Science Jobs
@not_boring_ds_jobs · 9,430
#86
AI Happens
@aihappens · 14,524
#87
(sci)Berloga Всех Наук и Технологий
@sberlogabig · 8,021
#88
̶с̶а̶м̶̶о̶изолента мёбиуса
@izolenta_mebiusa · 2,776
#89
e/acc
@cryptoEssay · 63,613
#90
Reliable ML
@reliable_ml · 6,606
#91
ODS Events
@datafest · 8,857
#92
Ppprompt | Sexy AI Prompts & Experiments | by @ponchiknews
@ppprompt · 15,553
#93
Linux Academy
@linuxacademiya · 28,647
#94
Нейронки и вино
@neural_wine · 1,934
#95

Read from Telegram’s recommendation API, most recently 10 August 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.

Appears in Telegram’s recommendations for other channels

The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.

Data Science | Machinelearning [ru]
@devsp · 19,659
Telegram ranks this channel #1 of 91 here — alongside 90 others — read 30 September 2026
Machine learning Interview
@machinelearning_interview · 30,560
Telegram ranks this channel #1 of 98 here — alongside 97 others — read 7 September 2026
Data Science
@datascienceiot · 42,496
Telegram ranks this channel #1 of 76 here — alongside 75 others — read 29 August 2026
Анализ данных (Data analysis)
@data_analysis_ml · 51,923
Telegram ranks this channel #1 of 93 here — alongside 92 others — read 25 August 2026
Python/ django
@pythonl · 58,287
Telegram ranks this channel #1 of 84 here — alongside 83 others — read 22 August 2026
Искусственный интеллект. Высокие технологии
@vistehno · 70,139
Telegram ranks this channel #1 of 94 here — alongside 93 others — read 20 August 2026
Python вопросы с собеседований
@python_job_interview · 24,906
Telegram ranks this channel #2 of 90 here — alongside 89 others — read 15 September 2026
Data Science. SQL hub
@sqlhub · 36,025
Telegram ranks this channel #2 of 87 here — alongside 86 others — read 2 September 2026
Data Secrets
@data_secrets · 96,035
Telegram ranks this channel #3 of 98 here — alongside 97 others — read 17 August 2026
Data Science Jobs
@datascienceml_jobs · 21,642
Telegram ranks this channel #4 of 97 here — alongside 96 others — read 24 September 2026
DevOps
@DevOPSitsec · 23,892
Telegram ranks this channel #4 of 90 here — alongside 89 others — read 18 September 2026
Yandex for ML
@yandexforml · 19,136
Telegram ranks this channel #7 of 96 here — alongside 95 others — read 4 October 2026
Sber AI
@SberAIScience · 26,512
Telegram ranks this channel #7 of 95 here — alongside 94 others — read 10 September 2026
Kali Linux
@linuxkalii · 55,842
Telegram ranks this channel #7 of 89 here — alongside 88 others — read 23 August 2026
XOR
@xor_journal · 195,549
Telegram ranks this channel #7 of 93 here — alongside 92 others — read 12 August 2026
gonzo-обзоры ML статей
@gonzo_ML · 24,695
Telegram ranks this channel #9 of 93 here — alongside 92 others — read 16 September 2026
Physics.Math.Code
@physics_lib · 146,676
Telegram ranks this channel #9 of 72 here — alongside 71 others — read 13 August 2026
Библиотека питониста | Python, Django, Flask
@pyproglib · 36,972
Telegram ranks this channel #10 of 84 here — alongside 83 others — read 1 September 2026
Data Science Jobs
@datasciencejobs · 22,587
Telegram ranks this channel #11 of 93 here — alongside 92 others — read 21 September 2026
LLM под капотом
@llm_under_hood · 30,219
Telegram ranks this channel #12 of 96 here — alongside 95 others — read 9 September 2026
эйай ньюз
@ai_newz · 98,262
Telegram ranks this channel #12 of 94 here — alongside 93 others — read 16 August 2026
GitHub Community
@github · 154,995
Telegram ranks this channel #14 of 87 here — alongside 86 others — read 12 August 2026
NOP::Nuances of Programming
@nuancesprog · 55,956
Telegram ranks this channel #15 of 84 here — alongside 83 others — read 23 August 2026
Сиолошная
@seeallochnaya · 81,254
Telegram ranks this channel #15 of 97 here — alongside 96 others — read 19 August 2026

This channel appears in 118 seed channels' Telegram-generated recommendation lists in total, of which the 24 where it ranks highest are shown above. Each is Telegram’s list for THAT channel, not this one — see how this is measured.

Cite this entry

A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 10 November 2026 — this entry's latest reading, not the date you are reading this.

“Machinelearning” (@ai_machinelearning_big_data), 275,659 subscribers as measured 10 November 2026. Telegram Register, tgregister.com/channel/ai_machinelearning_big_data.

Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.