Clustering & Unsupervised Learning in Python Discover Hidden Data Patterns: Master K-Means, Hierarchical Clustering, DBSCAN & E-Commerce Segmentation ⏱️ 4.9 hours ⭐ 3.88 👥 12,795 🔄 Mar 2025

Channel
Comidoc
@online_courses_tracker
On this record: Growth · Engagement · Posts · Telegram's recommendations · Cite this entry
2,364subscribers
-32 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
Register entry
| Telegram ID | -1001465555080 |
|---|---|
| Type | Channel |
| Username | @online_courses_tracker |
| Created | Between 1 April 2019 and 31 October 2021 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 3 September 2026 |
| Measurements held | 11 |
| Confirmed unchanged | 1 time, most recently 3 September 2026 |
| On Telegram | t.me/online_courses_tracker |
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 3 Sept 2026, 06:25 | 2,364 | -5 |
| 30 Aug 2026, 18:16 | 2,369 | -4 |
| 27 Aug 2026, 21:27 | 2,373 | -1 |
| 24 Aug 2026, 20:45 | 2,374 | -2 |
| 20 Aug 2026, 19:58 | 2,376 | -2 |
| 17 Aug 2026, 10:27 | 2,378 | -4 |
| 13 Aug 2026, 23:36 | 2,382 | -5 |
| 10 Aug 2026, 23:47 | 2,387 | -8 |
| 8 Aug 2026, 04:36 | 2,395 | -1 |
| 7 Aug 2026, 11:31 | 2,396 | no change |
| 7 Aug 2026, 11:29 | 2,396 | first reading |
Engagement
40 posts held, back to 7 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 2 pages of Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 0.306%
- avg views ÷ 2,364 subscribers
- Avg views / post
- 7.2
- 40 posts measured
- Reaction rate
- —
- this channel exposes no reaction counts
- Posts in window
- 40
- of 40 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.
| Window | Rolling 30 days · latest post in window 7 August 2026 |
|---|---|
| Posts held | 40 (7 August 2026 – 7 August 2026) |
| Views total | 289 |
| Reactions total | — |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 7 Aug 2026, 17:34 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.
Recent posts
Trello Mastery: Comprehensive Guide to Project Management Learn Trello's features, task management, collaboration, and automation to enhance your project management skills. ⏱️ 4.2 hours ⭐ 4.32 👥 11,330 🔄 May 2025
Modern Graph Theory Algorithms with Python Master NetworkX, Social Network Analysis & Shortest Path Algorithms - Build 4 Professional Projects with Graph Theory ⏱️ 2.4 hours ⭐ 3.82 👥 12,702 🔄 Feb 2025
Excel pour la Gestion de Projet : Outil Suivi & Dashboard Créez des tableaux de bord professionnels avec Excel : automatisation, reporting et KPIs pour un suivi efficace ⏱️ 2.1 hours ⭐ 3.77 👥 10,138 🔄 Mar 2025 🌐 French
Business Development with GenAI: Fuel Growth & Leadership Accelerate Business Growth with Generative AI: From Market Intelligence to Revenue Generation ⏱️ 4.0 hours ⭐ 4.19 👥 11,781 🔄 Nov 2025
Data-Centric Machine Learning with Python: Hands-On Guide Master data preprocessing, feature engineering, and ML modeling techniques with a hands-on loan prediction project. ⏱️ 3.6 hours ⭐ 4.13 👥 14,096 🔄 Mar 2025
SketchUp de A à Z : Maîtrisez la modélisation 3D Maîtrisez SketchUp de A à Z : Modélisation 3D complète, techniques pro et projets réels pour architectes et designers ⏱️ 3.4 hours ⭐ 4.32 👥 6,961 🔄 Mar 2025 🌐 French
Deploy ML Model in Production with FastAPI and Docker Learn ML deployment using FastAPI, Docker, CI/CD, and Cloud platforms ⏱️ 4.0 hours ⭐ 4.26 👥 15,238 🔄 May 2025
Python Microservices: Build, Scale, and Deploy like a Pro! Learn to build, secure, and scale Python microservices with FastAPI, Flask, Docker, and Kubernetes. ⏱️ 4.0 hours ⭐ 3.78 👥 17,126 🔄 May 2025
Build Progressive Web Apps: Python Django PWA Masterclass Master PWA development with Python & Django. Create offline-capable, responsive apps from setup to Play Store deployment ⏱️ 3.6 hours ⭐ 4.43 👥 13,599 🔄 May 2025
Excel Market Research Mastery: Data to Strategic Insights Transform raw data into powerful market insights using Excel. Learn data collection, visualization ⏱️ 4.1 hours ⭐ 4.42 👥 11,529 🔄 May 2025
AI-Driven Market Analysis: Predict & Profit with ML Models Leverage AI for Strategic Insights: Master Data Analysis, Predictive Modeling, Customer Segmentation & Sales Forecasting ⏱️ 3.5 hours ⭐ 4.34 👥 13,691 🔄 Apr 2025
Showing the 12 most recent of 40 posts we hold for @online_courses_tracker. 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.
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.
@elearninglinks · 34,466
Telegram ranks this channel #7 of 31 here — alongside 30 others — read 3 September 2026
@OPTION_CALL_TRADING · 38,946
Telegram ranks this channel #28 of 50 here — alongside 49 others — read 3 September 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 3 September 2026 — this entry's latest reading, not the date you are reading this.
“Comidoc” (@online_courses_tracker), 2,364 subscribers as measured 3 September 2026. Telegram Register, tgregister.com/channel/online_courses_tracker.
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.