5. Agentic Design
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Channel
@LearnAIMLStepbyStep
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Polls · Citations · Cite this entry
1,032subscribers
+2 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
| Telegram ID | -1002415336036 |
|---|---|
| Type | Channel |
| Username | @LearnAIMLStepbyStep |
| Created | Between 1 September 2024 and 18 March 2025— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 8 August 2026 |
| Last confirmed live | 23 August 2026 |
| Measurements held | 7 |
| Confirmed unchanged | 1 time, most recently 23 August 2026 |
| On Telegram | t.me/LearnAIMLStepbyStep |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 23 Aug 2026, 17:46 | 1,032 | +3 |
| 20 Aug 2026, 11:14 | 1,029 | -4 |
| 17 Aug 2026, 09:24 | 1,033 | +1 |
| 13 Aug 2026, 13:16 | 1,032 | +3 |
| 10 Aug 2026, 23:02 | 1,029 | -1 |
| 8 Aug 2026, 00:21 | 1,030 | no change |
| 7 Aug 2026, 22:44 | 1,030 | first reading |
20 posts held, back to 18 March 2025 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 pageof Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 29 August 2025. An engagement rate over an empty window would be a number about nothing.
Measured directly from 5 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.
70 reactions across 20 posts, in 4 distinct kinds. The most used accounts for 77.1% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 54 | 77.1% | |
| ❤ | 14 | 20.0% | |
| 👾 | 1 | 1.43% | |
| 🙏 | 1 | 1.43% |
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 20 of the 20 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 70reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 18 March 2025 to 29 August 2025, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
5. Agentic Design
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4. Prompt Engineering
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3. From Autocomplete to LLMs
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2. Building an Autocomplete
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1. Defining LLMs
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🔰 AI Agents: Building Teams of LLM Agents that Work For You
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🚀 Join "Neural Nexus" – Your Hub for AI & Machine Learning! 🤖🧠 👉 Join now: https://www.facebook.com/groups/1807029943182598 Are you passionate about AI, Deep Learning, and Neural Networks? Join our Facebook group "Neural Nexus" for: 🔥 Latest AI research & breakthroughs 📚 Free learning resources & tutorials 💡 Expert discussions & networking 🤖 Fun AI memes & challenges 👉 Join now: https://www.facebook.com…
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Which is a popular Deep Learning library?
Shares as published. No per-option vote count is published by Telegram, so none is shown.
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𝗟𝗼𝗴𝗶𝘀𝘁𝗶𝗰 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝘃𝘀 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗲𝗲𝘀
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𝗟𝗼𝗴𝗶𝘀𝘁𝗶𝗰 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝘃𝘀 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗲𝗲𝘀 When should you choose one over the other Both models are popular for binary classification tasks, but their assumptions and behavior are very different. Choosing the right one depends on data complexity, interpretability needs, and the structure of the input features. Here are some practical guidelines: - Use logistic regression when your features have a linear relationship…
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Hands-On AI_ RAG using LlamaIndex Part 01 to Part 04 Part 01: https://t.me/AIMLDeepThaught/959 Part 02: https://t.me/AIMLDeepThaught/967 Part 03: https://t.me/AIMLDeepThaught/975 Part 04: https://t.me/AIMLDeepThaught/979 1. Introduction 01. Overcome the limitations of LLMs with RAG 02. Limitations of LLMs 03. Use cases for retrieval-augmented generation RAG 2. Getting Started 01. Using GitHub Codespaces 02. Set…
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🤖 Join my Instagram for the latest updates on Machine Learning: https://www.instagram.com/aiml_neural_nexus/
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Showing the 12 most recent of 20 posts we hold for @LearnAIMLStepbyStep. 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.
The poll we hold for this entry, as Telegram rendered it when we read the post. A poll’s figures keep moving after that, so each one is dated.
Which is a popular Deep Learning library?
Shares as published. No per-option vote count is published by Telegram, so none is shown.
Percentages only — there are no per-option vote counts here, because Telegram publishes none.The public post preview gives each option’s share and a single voter total, and nothing else. Multiplying one by the other would produce a per-option tally that looks measured and is not: the shares are rounded to whole numbers before we ever see them. We print what was published and leave the column that does not exist empty.
The shares need not add up to 100.Rounding alone puts many polls at 99 or 101. A poll that allows more than one answer per voter runs well past 100 by design, and several here do. The bars are drawn against a fixed 100% track at each option’s own percentage rather than normalised to the total, so a poll that exceeds it shows that it does instead of being quietly rescaled.
Read from the 20 most recent posts we hold, published 18 March 2025 to 29 August 2025. Telegram labels each poll by kind — an anonymous poll, a quiz, a closed set of final results — and that label is reproduced rather than paraphrased.
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.
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 23 August 2026 — this entry's latest reading, not the date you are reading this.
“Learn Machine Learning and Data Analytics with Python” (@LearnAIMLStepbyStep), 1,032 subscribers as measured 23 August 2026. Telegram Register, tgregister.com/channel/LearnAIMLStepbyStep.
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.