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AI and Machine Learning

AI and Machine Learning

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Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses

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📈 Analytical overview of Telegram channel AI and Machine Learning

Channel AI and Machine Learning (@machine_learning_courses) in the English language segment is an active participant. Currently, the community unites 95 141 subscribers, ranking 1 512 in the Education category and 3 036 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 95 141 subscribers.

According to the latest data from 25 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 335 over the last 30 days and by -23 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 10.63%. Within the first 24 hours after publication, content typically collects 2.44% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 10 117 views. Within the first day, a publication typically gains 2 325 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 18.
  • Thematic interests: Content is focused on key topics such as learning, llm, linkedin, linux, udemy.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses

Thanks to the high frequency of updates (latest data received on 26 August, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

95 141
Subscribers
-2324 hours
+107 days
+33530 days
Attracting Subscribers
August '26
August '26
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in 2 channels
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in 16 channels
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June '26
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in 4 channels
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April '26
+792
in 19 channels
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March '26
+821
in 19 channels
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February '26
+1 063
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January '26
+1 309
in 18 channels
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December '25
+1 139
in 19 channels
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November '25
+1 241
in 20 channels
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October '25
+1 504
in 18 channels
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September '25
+2 028
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August '25
+2 762
in 19 channels
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July '25
+2 186
in 25 channels
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June '25
+1 398
in 17 channels
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May '25
+2 047
in 22 channels
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April '25
+1 711
in 24 channels
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March '25
+2 847
in 23 channels
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February '25
+3 008
in 22 channels
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January '25
+3 423
in 0 channels
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December '24
+4 022
in 4 channels
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November '24
+5 156
in 2 channels
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October '24
+2 670
in 21 channels
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September '24
+3 586
in 23 channels
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August '24
+3 742
in 21 channels
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July '24
+4 400
in 25 channels
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June '24
+4 554
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in 15 channels
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April '24
+1 997
in 1 channels
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March '24
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+1 950
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January '24
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in 3 channels
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December '23
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November '23
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+918
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January '23
+1 089
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December '22
+944
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November '22
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+581
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+885
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+826
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July '22
+844
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June '22
+804
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+780
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+145
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+160
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August '21
+143
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July '21
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June '21
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May '21
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Date
Subscriber Growth
Mentions
Channels
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07 August+11
06 August+49
05 August+26
04 August+3
03 August+20
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100 AI ML projects for all levels

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AI Agents vs Agentic AI... what’s the actual difference? There are 3 types of AI workflows worth knowing and each performs a+1
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6 265
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AI Concepts Explained
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9 888
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Most Data Scientists structure their projects wrong. Use this clean, production-ready layout👇 1️⃣ config/ – config files Sep
Most Data Scientists structure their projects wrong. Use this clean, production-ready layout👇 1️⃣ config/ – config files Separate params from code (local.yaml, prod.yaml) 2️⃣ data/ – full data lifecycle raw → preprocessed → features → predictions 3️⃣ entrypoint/ – main scripts train.py (pipeline) inference.py (batch/real-time) 4️⃣ notebooks/ – exploration only EDA, analysis — never production logic 5️⃣ src/ – core ML code feature engineering, training, inference (modular + testable) 6️⃣ tests/ – automated checks prevent silent failures 7️⃣ docker + env files – reproducibility same setup on any machine/CI 8️⃣ pinned dependencies – stability exact versions → consistent results
9 691
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4 stages of LLM Training+1
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11 038
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Key Nodes in n8n Most people think AI automation is complex, but with n8n it comes down to just 7 building blocks. 1️⃣ Code N+6
Key Nodes in n8n Most people think AI automation is complex, but with n8n it comes down to just 7 building blocks. 1️⃣ Code Node → custom logic 2️⃣ HTTP Request → connect any API 3️⃣ Edit Fields → clean data 4️⃣ IF Node → conditional paths 5️⃣ Switch Node → handle multiple cases 6️⃣ Loop Over Items → process lists 7️⃣ Error Handling → keep workflows alive n8n makes it simple: drag, drop, connect.
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📱Artificial intelligence 📱Hands-On AI: Building Your First LLM-Powered App
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🔅 Hands-On AI: Building Your First LLM-Powered App 📝 Get started building apps powered by large language models (LLMs) in t
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11 233
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All major LLMs, one login: glbgpt.com GPT-5 · Claude · Gemini · Grok · DeepSeek. Crypto top-up (USDT), pay-as-you-go, no sub
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3 134
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📕 The Ultimate Roadmap to master AI Agents
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🔢 Stages of LLM Training Training a Large Language Model isn’t a single step—it’s a journey through multiple stages that sha
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🧠 Code with an AI agent — inside Telegram BrainDaemon is a private AI coding workspace (Mini App): • Cloud machine + real to
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