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 Syria225 Technologies & Applications3 331...

📈 Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 334 subscribers, ranking 3 331 in the Technologies & Applications category and 225 in the Syria region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 334 subscribers.

According to the latest data from 10 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 383 over the last 30 days and by 25 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 2.35%. Within the first 24 hours after publication, content typically collects 1.95% reactions from the total number of subscribers.
Post reach: On average, each post receives 948 views. Within the first day, a publication typically gains 786 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 4.
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 11 July, 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.

40 334

Subscribers

+2524 hours

+1227 days

+38330 days

948

Post views

~ 78624 hours

~ 1 06648 hours

2.35%

Engagement rate

~ 2

Posts per day

Ads index

beta

Posts Archive

40 334

📌 Step-by-Step Guide to Build and Deploy an LLM-Powered Chat with Memory in Streamlit 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-05-01 | ⏱️ Read time: 17 min read And monitor your API usage on Google Cloud Console

40 334

📌 A Farewell to APMs — The Future of Observability is MCP tools 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-01 | ⏱️ Read time: 10 min read Like many other fields, the world of observability is about to be turned upside down

40 334

📌 Rust for Python Developers: Why You Should Take a Look at the Rust Programming Language 🗂 Category: PROGRAMMING 🕒 Date: 2025-05-02 | ⏱️ Read time: 13 min read Discover how Rust complements Python with speed, safety, and control — and why it’s worth…

40 334

📌 Agentic AI 101: Starting Your Journey Building AI Agents 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-02 | ⏱️ Read time: 12 min read Learn the fundamentals of how to create AI Agents.

40 334

📌 Talking to Kids About AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-02 | ⏱️ Read time: 16 min read “This is your brain on an LLM”, and other things you shouldn’t say

40 334

📌 Want Better Clusters? Try DeepType 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-02 | ⏱️ Read time: 9 min read A smarter way to cluster data using deep learning

40 334

📌 The Difference between Duplicate and Reference in Power Query 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-05-02 | ⏱️ Read time: 9 min read In Power Query, we can duplicate or reference existing tables. But what are the differences…

40 334

📌 Why I stopped Using Cursor and Reverted to VSCode 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-02 | ⏱️ Read time: 6 min read Is GitHub Copilot the best AI-assistant for Data Scientists?

40 334

📌 The Shape‑First Tune‑Up Provides Organizations with a Means to Reduce MongoDB Expenses by 79% 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-05-02 | ⏱️ Read time: 9 min read A real-world engineering fix that saved over $12K/month on MongoDB without upgrading infrastructure.

40 334

📌 Attaining LLM Certainty with AI Decision Circuits 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-05-02 | ⏱️ Read time: 15 min read Uncertainty is nothing new in technology — all modern systems overcome uncertain inputs and outputs…

40 334

📌 Build and Query Knowledge Graphs with LLMs 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-05-02 | ⏱️ Read time: 28 min read Going from document ingestion to smart queries — all with open tools and guided setup

40 334

📌 From a Point to L∞ 🗂 Category: MATH 🕒 Date: 2025-05-02 | ⏱️ Read time: 9 min read How AI uses distance

40 334

📌 Website Feature Engineering at Scale: PySpark, Python & Snowflake 🗂 Category: DATA SCIENCE 🕒 Date: 2025-05-05 | ⏱️ Read time: 9 min read Introduction and Problem Imagine you’re staring at a database containing thousands of merchants across multiple…

40 334

📌 Fine-Tuning vLLMs for Document Understanding 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-05-05 | ⏱️ Read time: 25 min read Learn how you can fine-tune visual language models for specific tasks

40 334

📌 Making Sense of KPI Changes 🗂 Category: DATA SCIENCE 🕒 Date: 2025-05-05 | ⏱️ Read time: 15 min read A practical guide to understanding what’s really going on

40 334

📌 Diffusion Models, Explained Simply 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-05 | ⏱️ Read time: 7 min read From noise to art: how to generate high-quality images using diffusion models

40 334

📌 The CNN That Challenges ViT | ConvNeXt 🗂 Category: DEEP LEARNING 🕒 Date: 2025-05-05 | ⏱️ Read time: 24 min read A PyTorch implementation on the ConvNeXt architecture

40 334

📌 Think. Know. Act. How AI’s Core Capabilities Will Shape the Future of Work 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-06 | ⏱️ Read time: 13 min read It’s not just about technical depth, it’s about strategic clarity

40 334

📌 Benchmarking Tabular Reinforcement Learning Algorithms 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-05-06 | ⏱️ Read time: 27 min read Comparing all methods from Part I of Sutton’s book on gridworld environments

40 334

📌 Make Your Data Move: Creating Animations in Python for Science and Machine Learning 🗂 Category: DATA VISUALIZATION 🕒 Date: 2025-05-06 | ⏱️ Read time: 6 min read Go beyond static plots with matplotlib.