AI and Machine Learning
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Show moreπ 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 94 021 subscribers, ranking 1 561 in the Education category and 3 020 in the India region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 94 021 subscribers.
According to the latest data from 24 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 986 over the last 30 days and by 67 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 6.50%. Within the first 24 hours after publication, content typically collects 1.56% reactions from the total number of subscribers.
- Post reach: On average, each post receives 6 109 views. Within the first day, a publication typically gains 1 470 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 8.
- Thematic interests: Content is focused on key topics such as learning, llm, linkedin, linux, udemy.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βLearn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more!
Buy ads: https://telega.io/c/machine_learning_coursesβ
Thanks to the high frequency of updates (latest data received on 25 June, 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.
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All images are from the book AI Engineering unless otherwise credited.β° Timestamps 00:00 What is AI Engineering? 01:49 Understanding Foundation Models 08:40 Evaluating AI Models 14:50 Model Selection 23:15 Prompt Engineering 30:20 RAG and Context Construction 36:56 Agents and Memory Systems 43:02 Finetuning 52:40 Dataset Engineering 59:45 Inference Optimization 01:09:01 Architecture and User Feedback
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