Telegram RegisterThe public register of Telegram

Channel

Machine Learning

@MachineLearning9

On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry

40,898subscribers

+72 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 31,623–100,000.

Register entry

Telegram ID-1001922388839
TypeChannel
Username@MachineLearning9
DescriptionReal Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho
CreatedBetween 1 April 2023 and 31 October 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live12 August 2026
Measurements held7
Confirmed unchanged1 time, most recently 12 August 2026
On Telegramt.me/MachineLearning9

Growth

40,82640,89840,8626 August 2026 — 40,826 subscribers6 August 2026 — 40,826 subscribers7 August 2026 — 40,853 subscribers8 August 2026 — 40,866 subscribers10 August 2026 — 40,878 subscribers11 August 2026 — 40,893 subscribers12 August 2026 — 40,898 subscribers6 August 202612 August 2026
7 measurements spanning 6 days, net +72. 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 40,815–40,909 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
12 Aug 2026, 14:2440,898+5
11 Aug 2026, 12:5340,893+15
10 Aug 2026, 09:5040,878+12
8 Aug 2026, 09:0240,866+13
7 Aug 2026, 07:3740,853+27
6 Aug 2026, 10:4040,826no change
6 Aug 2026, 09:2140,826first reading

Engagement

30 posts held, back to 27 July 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 17 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
3.56%
avg views ÷ 40,898 subscribers
Avg views / post
1,460
28 posts measured
Reaction rate
0.257%
reactions ÷ views · ER floor
Posts in window
30
of 30 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. It is computed over the 25 of 28 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 12 August 2026
Posts held30 (27 July 202612 August 2026)
Views total40,803
Reactions total98
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken12 Aug 2026, 20:27 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
3,650
Videos
33
Links
689

Lifetime counters from Telegram’s own channel header, read 12 August 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
1m 34s
Average length
31s

Measured directly from 3 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

98 reactions across 25 posts, in 2 distinct kinds. The most used accounts for 94.9% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
9394.9%
👍55.10%

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 25 of the 30 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 98reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 30 most recent posts we hold, published 27 July 2026 to 12 August 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.

Recent posts

12 Aug 2026, 06:42 UTC422 views5 reactionsread 12 August 2026
Forwarded from @CodeProgrammerFile

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5

11 Aug 2026, 21:15 UTC653 views2 reactionsread 12 August 2026
Forwarded from @Udemy26Photo

🔔 Still Available! 400 Machine Learning Interview Questions with Answers 2026 Machine LearningnInterview Questions Practice Test | Freshers to Experienced | Detailed Explanations for Each Question… 🌍 Language: English (US) 👥 Students: 205 students ⭐️ Rating: 0.0/5.0 (0 reviews) 🏃‍♂️ Enrollments Left: 1 ⏳ Expires In: 0D:30H:30M 💰 Price: $23.03 ⟹ FREE 🆔 Coupon: 58223770053913048BEB ⚡ Opens instantly — your free lin

2

11 Aug 2026, 13:17 UTC789 viewsread 12 August 2026
Forwarded from @Jobs204Photo

🤖 AI & Machine Learning AI/ML Engineer (m/w/d) – Generative AI + Legal AI 🏢 YPOG 📍 Berlin, Berlin, Germany · 🌐 Remote 🕒 14 hours ago Tap below to view the full posting and apply directly on LinkedIn. 💎 Curated by: https://t.me/Jobs204

11 Aug 2026, 06:07 UTC≈1,060 views5 reactionsread 12 August 2026
Photo

"The Mathematics of Bitcoin" is a concise work that analyzes Bitcoin from a mathematical perspective. 📊 It utilizes probability theory, stochastic processes, martingales, combinatorics, and special functions to explore the mechanisms of the Bitcoin protocol. 🧮 In particular, the authors examine the probability of double-spending, the profitability of mining, block generation, miner strategies, and the resilience of

👍32

11 Aug 2026, 05:45 UTC505 views4 reactionsread 12 August 2026
Forwarded from @CodeProgrammerPhoto

🧿ANTHROPH\C Launches 13 Free AI Courses Anthropic Academy has announced 13 free AI courses. The courses cover the following topics: • Working with Claude • AI Fundamentals • AI Agents • Model Context Protocol (MCP) • Claude Code • Working with APIs • Enterprise AI • Google Cloud Vertex AI and Amazon Bedrock Integration 1. Claude 101 2. AI Fluency: Framework & Foundations 3. Introduction to Agent Skills 4. Buil

4

10 Aug 2026, 15:32 UTC819 views2 reactionsread 12 August 2026
Photo

🔥 Land Your Dream Job – Free Interview Prep Resources Inside! 🌈Struggling with tough interview questions? Nervous about technical grilling? You're not alone. We've just released a bunch of 100% free interview prep kits for 2026 – covering common Q&As, behavioral questions, technical deep-dives, and role-specific tips for #Cisco, #AWS, #PMP, #AI, #Python, #Excel, and #Cybersecurity. 💥No signup traps, no hidden fees

2

10 Aug 2026, 08:59 UTC395 viewsread 12 August 2026
Forwarded from @CodeProgrammer

🚨 SURPRISE ALERT! 🚨 Stop paying full price on Udemy. Seriously. 💸 I built a bot that hunts down 100% FREE Udemy coupons 24/7 — while you sleep, eat, or scroll. 🎯 Here's the magic: 📚 Mini App catalog — every active free coupon in one place 🔔 Auto-push — new courses land straight in your chat 📢 Live channel — never miss a deal Why it matters? Most people pay $200+ for courses you can grab for $0 — if you know wher

10 Aug 2026, 06:32 UTC739 views2 reactionsread 12 August 2026
Forwarded from @CodeProgrammerPhoto

🔖 A comprehensive collection of resources on MLOps We've found a useful collection for those who want to go beyond just training models and delve into their deployment and operation. This repository contains the best materials on deploying, monitoring, automating ML pipelines, CI/CD, and other practices that are essential for production systems. ⛓ Link to GitHub https://github.com/visenger/awesome-mlops

2

8 Aug 2026, 11:22 UTC≈1,260 views5 reactionsread 12 August 2026
Photo

🔖 Google DeepMind has released a book titled "How to Scale Your Model." It explains how to scale and deploy models even with limited computing resources. It's useful for those who work on optimizing and deploying ML models. ⛓ Link to the book https://jax-ml.github.io/scaling-book

5

8 Aug 2026, 09:09 UTC864 views2 reactionsread 12 August 2026
Forwarded from @Python53Photo

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2

8 Aug 2026, 07:04 UTC≈2,910 views4 reactionsread 12 August 2026

"Introduction to Machine Learning" is another free textbook on machine learning, approximately 600 pages long, which emphasizes a deep mathematical understanding of the subject. 📚🧮 The book begins with the mathematical foundations necessary for further study: linear algebra, mathematical analysis, probability theory, matrix analysis, and optimization methods. It then covers the main supervised learning algorithms: l

3👍1

Showing the 12 most recent of 30 posts we hold for @MachineLearning9. 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.

Citation-graph rank

Citation-graph rank — 116,074 of 1,160,990entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.

Forward network

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

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.

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.

Linkedin Learning
@linkedin_learning · 218,111
Telegram ranks this channel #16 of 65 here — alongside 64 others — read 11 August 2026
Computer Science and Programming
@computer_science_and_programming · 140,958
Telegram ranks this channel #26 of 87 here — alongside 86 others — read 13 August 2026

This channel appears in 2 seed channels' Telegram-generated recommendation lists in total. 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 12 August 2026 — this entry's latest reading, not the date you are reading this.

“Machine Learning” (@MachineLearning9), 40,898 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/MachineLearning9.

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.