100 Machine Learning Interview Questions and Answers 🤖 ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
❤1

Channel
@MachineLearning9
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry
41,624subscribers
+798 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 31,623–100,000.
| Telegram ID | -1001922388839 |
|---|---|
| Type | Channel |
| Username | @MachineLearning9 |
| Description | Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho |
| Created | Between 1 April 2023 and 31 October 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 4 October 2026 |
| Measurements held | 34 |
| Confirmed unchanged | 1 time, most recently 4 October 2026 |
| On Telegram | t.me/MachineLearning9 |
Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 9 September 2026 and assigned it the closest of 31 fixed categories, at 99% 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 why it has no browse page of its own yet.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 4 Oct 2026, 19:38 | 41,624 | +200 |
| 27 Sept 2026, 01:17 | 41,424 | +135 |
| 17 Sept 2026, 15:18 | 41,289 | +32 |
| 15 Sept 2026, 16:20 | 41,257 | +24 |
| 14 Sept 2026, 00:57 | 41,233 | +82 |
| 12 Sept 2026, 09:15 | 41,151 | +11 |
| 10 Sept 2026, 02:17 | 41,140 | +39 |
| 4 Sept 2026, 11:39 | 41,101 | +25 |
| 3 Sept 2026, 01:16 | 41,076 | +23 |
| 2 Sept 2026, 02:45 | 41,053 | +8 |
| 1 Sept 2026, 03:38 | 41,045 | +14 |
| 31 Aug 2026, 00:38 | 41,031 | +39 |
| 30 Aug 2026, 02:04 | 40,992 | +14 |
| 29 Aug 2026, 04:28 | 40,978 | +4 |
| 28 Aug 2026, 01:36 | 40,974 | +4 |
| 27 Aug 2026, 02:27 | 40,970 | -6 |
| 26 Aug 2026, 02:24 | 40,976 | -1 |
| 25 Aug 2026, 00:22 | 40,977 | -22 |
| 23 Aug 2026, 08:43 | 40,999 | +2 |
| 21 Aug 2026, 22:17 | 40,997 | first reading |
105 posts held, back to 27 July 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 123 pages of Telegram’s post history, 20 posts per page.
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 31 of 34 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 7 October 2026 |
|---|---|
| Posts held | 105 (27 July 2026 – 7 October 2026) |
| Views total | 71,672 |
| Reactions total | 158 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 7 Oct 2026, 08:42 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.
Lifetime counters from Telegram’s own channel header, read 7 October 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.
Measured directly from 7 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.
383 reactions across 83 posts, in 4 distinct kinds. The most used accounts for 89.6% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 343 | 89.6% | |
| 👍 | 32 | 8.36% | |
| 👎 | 5 | 1.31% | |
| 🔥 | 3 | 0.783% |
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 83 of the 105 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 383 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 105 most recent posts we hold, published 27 July 2026 to 7 October 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.
100 Machine Learning Interview Questions and Answers 🤖 ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
❤1
Hands On Python Data Science - Data Science Bootcamp Master Python for Data Science with Real-World Applications: Dive Deep into Data Analysis, Machine Learning 🏷 Category: Development 🌍 Language: English 👥 Students: 31,524 students ⭐️ Rating: 4.3/5.0 💰 Price: $14.99 ⟹ FREE 🆔 Coupon: •••••••••• (tap below to reveal) 🔓 Tap "Get Coupon" below — the code unlocks inside the app after a short rewarded ad. 💎 By: https:…
"Understanding Transformers and Attention Mechanisms" - a concise mathematical introduction to the attention mechanism, one of the key ideas in modern language models. This document explains tokenization and embeddings, queries, keys, and values, attention scores and weights, multi-head attention, self-attention, causal attention and masking, cross-attention, and the basic structure of the Transformer architecture. …
❤3
Machine Learning pinned «https://t.me/UdemySybot?start=ref_418788114 Get Free Courses 😁»
📚 "Fundamentals of Computer Vision" is a free online book published by MIT Press, providing a broad introduction to computer vision from the perspectives of image processing and machine learning. 🔍 It covers topics such as image formation, training and backpropagation, image filtering and Fourier analysis, CNNs, RNNs, and transformers, generative models, representation learning, 3D geometry, motion estimation, objec…
❤6
https://t.me/UdemySybot?start=ref_418788114 Get Free Courses 😁
❤2
Normalization vs Standardization 📊 Why they are not the same. One page: the two formulas side by side, two columns from the same table on incompatible scales, the same 400 values shown raw / min-max / standardized so you can see only the location and scale move while the skew stays, a worked example on five numbers, and a "which one, when" guide. The bottom line: ask what the next step assumes — a fixed interval me…
❤2
I found a great resource for interactive learning about machine learning and AI – VizLearn. You can experiment with gradient descent, SVM, PCA, the Bayesian method, BPE tokenization, Q/K/V, KV-cache, quantization, and much more. You can change the input data and see how the calculations themselves change. It's free and doesn't require registration. https://vizlearn.in https://t.me/MachineLearning9
❤8
"Learning Mathematics: Why Memory, Practice, and Technique are More Important than Talent" To master mathematics, it is necessary to memorize a large amount of information, rules, methods of simplification, and problem-solving techniques. There is no other way. Theory is useful, but in moderation. It's like learning a language. If you focus too much on grammar, you will never learn to speak it fluently. https://al…
👍3❤1
This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer
❤2
"How to Train a Neural Network" is a concise summary of the MIT course lectures on deep learning from 2024. It focuses on one of the fundamental questions in neural networks: how a model learns its weights. The summary examines the training process from a mathematical perspective. It covers topics such as forward propagation, loss functions, gradients, backpropagation, and gradient-based optimization methods. I bel…
❤2
"Trigonometry" is a free, open-source textbook on trigonometry, with over 1000 pages, covering the subject from basic concepts to advanced topics. The book covers angles and triangles, trigonometric relationships, the unit circle, sine, cosine, and tangent functions, graphs and their transformations, radians, solving triangles, the sine and cosine theorems, trigonometric identities and equations, inverse trigonometr…
👍6
Showing the 12 most recent of 105 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.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Republishes
Channels on the register whose posts this channel has forwarded.
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.
Named by 2 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.
Named by
Channels on the register whose posts name this channel's handle.
Names
Channels on the register whose handles appear in this channel's 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.
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.
Read from Telegram’s recommendation API, most recently 30 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.
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.
This channel appears in 107 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.
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 4 October 2026 — this entry's latest reading, not the date you are reading this.
“Machine Learning” (@MachineLearning9), 41,624 subscribers as measured 4 October 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.