Machine Learning

Open in Telegram

Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

Network:Machine Learning with Python Syria231 Technologies & Applications3 384...

📈 Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 106 subscribers, ranking 3 384 in the Technologies & Applications category and 231 in the Syria region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 106 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 401 over the last 30 days and by 38 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 1.96%. Within the first 24 hours after publication, content typically collects 1.16% reactions from the total number of subscribers.
Post reach: On average, each post receives 788 views. Within the first day, a publication typically gains 465 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
Thematic interests: Content is focused on key topics such as distance, insidead, gpu, learning, degree.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho”

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 Technologies & Applications category.

40 106

Subscribers

+3824 hours

+637 days

+40130 days

788

Post views

~ 46524 hours

~ 62048 hours

1.96%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

40 109

🎁❗️TODAY FREE❗️🎁 Entry to our VIP channel is completely free today. Tomorrow it will cost $500! 🔥 JOIN 👇 https://t.me/+DBdNGbxImzgxMDBi https://t.me/+DBdNGbxImzgxMDBi https://t.me/+DBdNGbxImzgxMDBi

40 109

📌 Topic Modeling Techniques for 2026: Seeded Modeling, LLM Integration, and Data Summaries 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-14 | ⏱️ Read time: 15 min read Seeded topic modeling, integration with LLMs, and training on summarized data are the fresh parts… #DataScience #AI #Python

40 109

📌 Glitches in the Attention Matrix 🗂 Category: DEEP LEARNING 🕒 Date: 2026-01-14 | ⏱️ Read time: 13 min read A history of Transformer artifacts and the latest research on how to fix them #DataScience #AI #Python

40 109

Repost from Machine Learning with Python

Do you want to teach AI on real projects? In this #repository, there are 29 projects with Generative #AI,#MachineLearning, and #Deep +Learning. With full #code for each one. This is pure gold: https://github.com/KalyanM45/AI-Project-Gallery 👉 https://t.me/CodeProgrammer

40 109

📌 What Is a Knowledge Graph — and Why It Matters 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-14 | ⏱️ Read time: 18 min read How structured knowledge became healthcare’s quiet advantage #DataScience #AI #Python

40 109

📌 Why Human-Centered Data Analytics Matters More Than Ever 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-14 | ⏱️ Read time: 8 min read From optimizing metrics to designing meaning: putting people back into data-driven decisions #DataScience #AI #Python

40 109

📌 From ‘Dataslows’ to Dataflows: The Gen2 Performance Revolution in Microsoft Fabric 🗂 Category: DATA ENGINEERING 🕒 Date: 2026-01-13 | ⏱️ Read time: 8 min read Dataflows were (rightly?) considered “the slowest and least performant option” for ingesting data into Power… #DataScience #AI #Python

40 109

📌 An introduction to AWS Bedrock 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-13 | ⏱️ Read time: 13 min read The how, why, what and where of Amazon’s LLM access layer #DataScience #AI #Python

40 109

Repost from Machine Learning with Python

⚡️ All cheat sheets for programmers in one place. There's a lot of useful stuff inside: short, clear tips on languages, technologies, and frameworks. No registration required and it's free. https://overapi.com/ #python #php #Database #DataAnalysis #MachineLearning #AI #DeepLearning #LLMS https://t.me/CodeProgrammer ⚡️

40 109

📌 How to Maximize Claude Code Effectiveness 🗂 Category: AGENTIC AI 🕒 Date: 2026-01-13 | ⏱️ Read time: 9 min read Learn how to get the most out of agentic coding #DataScience #AI #Python

40 109

📌 Why Your ML Model Works in Training But Fails in Production 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-13 | ⏱️ Read time: 8 min read Hard lessons from building production ML systems where data leaks, defaults lie, populations shift, and… #DataScience #AI #Python

40 109

📌 Under the Uzès Sun: When Historical Data Reveals the Climate Change 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-13 | ⏱️ Read time: 11 min read Longer summers, milder winters: analysis of temperature trends in Uzès, France, year after year. #DataScience #AI #Python

40 109

📌 Optimizing Data Transfer in Batched AI/ML Inference Workloads 🗂 Category: DATA ENGINEERING 🕒 Date: 2026-01-12 | ⏱️ Read time: 13 min read A deep dive on data transfer bottlenecks, their identification, and their resolution with the help… #DataScience #AI #Python

40 109

📌 When Does Adding Fancy RAG Features Work? 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-12 | ⏱️ Read time: 23 min read Looking at the performance of different pipelines #DataScience #AI #Python

40 109

📌 Why 90% Accuracy in Text-to-SQL is 100% Useless 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-12 | ⏱️ Read time: 9 min read The eternal promise of self-service analytics #DataScience #AI #Python

40 109

Repost from Machine Learning with Python

These Google Colab-notebooks help to implement all machine learning algorithms from scratch 🤯 Repo: https://udlbook.github.io/udlbook/ 👉 @codeprogrammer

40 109

try this bot ai https://t.me/Creator_Ai_Tech_Bot?start=109697-ae026840

40 109

📌 How AI Can Become Your Personal Language Tutor 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-12 | ⏱️ Read time: 11 min read How I used n8n to build AI study partners for learning Mandarin: vocabulary, listening, and… #DataScience #AI #Python

40 109

Repost from Machine Learning with Python

🧠 𝐊-𝐍𝐞𝐚𝐫𝐞𝐬𝐭 𝐍𝐞𝐢𝐠𝐡𝐛𝐨𝐫𝐬 (𝐊𝐍𝐍)⁣ 🔹 𝐖𝐡𝐚𝐭 𝐈 𝐜𝐨𝐯𝐞𝐫𝐞𝐝 𝐭𝐨𝐝𝐚𝐲⁣ 𝐖𝐡𝐚𝐭 𝐊𝐍𝐍 𝐢𝐬 𝐚𝐧𝐝 𝐡𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬⁣ 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐊𝐍𝐍 𝐟𝐨𝐫 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐯𝐬 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧⁣ 𝐑𝐨𝐥𝐞 𝐨𝐟 𝐊 (𝐡𝐲𝐩𝐞𝐫𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫)⁣ 𝐃𝐢𝐬𝐭𝐚𝐧𝐜𝐞 𝐦𝐞𝐭𝐫𝐢𝐜𝐬: 𝐄𝐮𝐜𝐥𝐢𝐝𝐞𝐚𝐧 𝐯𝐬 𝐌𝐚𝐧𝐡𝐚𝐭𝐭𝐚𝐧⁣ 𝐖𝐡𝐲 𝐊𝐍𝐍 𝐢𝐬 𝐜𝐚𝐥𝐥𝐞𝐝 𝐚 𝐥𝐚𝐳𝐲 / 𝐢𝐧𝐬𝐭𝐚𝐧𝐜𝐞-𝐛𝐚𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐞𝐫⁣ ⁣ 🎯 𝐓𝐨𝐩 𝟏𝟎 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 (𝐌𝐮𝐬𝐭-𝐊𝐧𝐨𝐰)⁣ ⁣ 1️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘒-𝘕𝘦𝘢𝘳𝘦𝘴𝘵 𝘕𝘦𝘪𝘨𝘩𝘣𝘰𝘳𝘴 (𝘒𝘕𝘕)?⁣ 2️⃣ 𝘞𝘩𝘺 𝘪𝘴 𝘒𝘕𝘕 𝘤𝘢𝘭𝘭𝘦𝘥 𝘢 𝘭𝘢𝘻𝘺 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘢𝘭𝘨𝘰𝘳𝘪𝘵𝘩𝘮?⁣ 3️⃣ 𝘋𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘒𝘕𝘕 𝘤𝘭𝘢𝘴𝘴𝘪𝘧𝘪𝘤𝘢𝘵𝘪𝘰𝘯 𝘢𝘯𝘥 𝘒𝘕𝘕 𝘳𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯?⁣ 4️⃣ 𝘏𝘰𝘸 𝘥𝘰 𝘺𝘰𝘶 𝘤𝘩𝘰𝘰𝘴𝘦 𝘵𝘩𝘦 𝘷𝘢𝘭𝘶𝘦 𝘰𝘧 𝘒?⁣ 5️⃣ 𝘞𝘩𝘢𝘵 𝘩𝘢𝘱𝘱𝘦𝘯𝘴 𝘸𝘩𝘦𝘯 𝘒 𝘪𝘴 𝘵𝘰𝘰 𝘴𝘮𝘢𝘭𝘭 𝘰𝘳 𝘵𝘰𝘰 𝘭𝘢𝘳𝘨𝘦?⁣ 6️⃣ 𝘞𝘩𝘢𝘵 𝘥𝘪𝘴𝘵𝘢𝘯𝘤𝘦 𝘮𝘦𝘵𝘳𝘪𝘤𝘴 𝘢𝘳𝘦 𝘤𝘰𝘮𝘮𝘰𝘯𝘭𝘺 𝘶𝘴𝘦𝘥 𝘪𝘯 𝘒𝘕𝘕?⁣ 7️⃣ 𝘞𝘩𝘺 𝘥𝘰𝘦𝘴 𝘒𝘕𝘕 𝘱𝘦𝘳𝘧𝘰𝘳𝘮 𝘱𝘰𝘰𝘳𝘭𝘺 𝘰𝘯 𝘩𝘪𝘨𝘩-𝘥𝘪𝘮𝘦𝘯𝘴𝘪𝘰𝘯𝘢𝘭 𝘥𝘢𝘵𝘢?⁣ 8️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 𝘵𝘪𝘮𝘦 𝘤𝘰𝘮𝘱𝘭𝘦𝘹𝘪𝘵𝘺 𝘰𝘧 𝘒𝘕𝘕?⁣ 9️⃣ 𝘏𝘰𝘸 𝘥𝘰 𝘒𝘋-𝘛𝘳𝘦𝘦 𝘢𝘯𝘥 𝘉𝘢𝘭𝘭-𝘛𝘳𝘦𝘦 𝘪𝘮𝘱𝘳𝘰𝘷𝘦 𝘒𝘕𝘕 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦?⁣ 🔟 𝘞𝘩𝘦𝘯 𝘴𝘩𝘰𝘶𝘭𝘥 𝘺𝘰𝘶 𝘢𝘷𝘰𝘪𝘥 𝘶𝘴𝘪𝘯𝘨 #𝘒𝘕𝘕?⁣ https://t.me/CodeProgrammer ⭐️

40 109

📌 How to Leverage Slash Commands to Code Effectively 🗂 Category: LLM APPLICATIONS 🕒 Date: 2026-01-11 | ⏱️ Read time: 8 min read Learn how I utilize slash commands to be a more efficient engineer #DataScience #AI #Python