Machine Learning

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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 Syria232 Technologies & Applications3 398...

📈 Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 072 subscribers, ranking 3 398 in the Technologies & Applications category and 232 in the Syria region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 072 subscribers.

According to the latest data from 23 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 379 over the last 30 days and by 30 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 1.92%. 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 770 views. Within the first day, a publication typically gains 466 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
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 24 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.

40 072

Subscribers

+3024 hours

+337 days

+37930 days

770

Post views

~ 46624 hours

~ 62048 hours

1.92%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

40 068

📌 How to Become an AI Engineer Fast (Skills, Projects, Salary) 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-29 | ⏱️ Read time: 12 min read Spoiler, it will take longer than 3 months #DataScience #AI #Python

40 068

📌 Self-Healing Neural Networks in PyTorch: Fix Model Drift in Real Time Without Retraining 🗂 Category: DEEP LEARNING 🕒 Date: 2026-03-29 | ⏱️ Read time: 22 min read What happens when your production model drifts and retraining isn’t an option? This article shows… #DataScience #AI #Python

40 068

📌 Using OpenClaw as a Force Multiplier: What One Person Can Ship with Autonomous Agents 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-28 | ⏱️ Read time: 25 min read It’s easier than ever to 10x your output with agentic AI. #DataScience #AI #Python

40 068

📌 From NetCDF to Insights: A Practical Pipeline for City-Level Climate Risk Analysis 🗂 Category: CLIMATE CHANGE 🕒 Date: 2026-03-28 | ⏱️ Read time: 7 min read Integrating CMIP6 projections, ERA5 reanalysis, and impact models into a lightweight, interpretable workflow #DataScience #AI #Python

40 068

📌 How ElevenLabs Voice AI Is Replacing Screens in Warehouse and Manufacturing Operations 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-27 | ⏱️ Read time: 10 min read A warehouse picking operation is the process of collecting items from storage locations to fulfil… #DataScience #AI #Python

40 068

📌 A Beginner’s Guide to Quantum Computing with Python 🗂 Category: QUANTUM COMPUTING 🕒 Date: 2026-03-27 | ⏱️ Read time: 7 min read Simulate a quantum computer with Qiskit #DataScience #AI #Python

40 068

📌 Building a Production-Grade Multi-Node Training Pipeline with PyTorch DDP 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-27 | ⏱️ Read time: 14 min read A practical, code-driven guide to scaling deep learning across machines — from NCCL process groups… #DataScience #AI #Python

40 068

Repost from Machine Learning with Python

Classical filters & convolution: The heart of computer vision Before Deep Learning exploded onto the scene, traditional computer vision centered on filters. Filters were small, hand-engineered matrices that you convolved with an image to detect specific features like edges, corners, or textures. In this article, we will dive into the details of classical filters and convolution operation - how they work, why they matter, and how to implement them. More: https://www.vizuaranewsletter.com/p/classical-filters-and-convolution

40 068

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40 068

📌 What the Bits-over-Random Metric Changed in How I Think About RAG and Agents 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-26 | ⏱️ Read time: 19 min read Why retrieval that looks excellent on paper can still behave like noise in real RAG… #DataScience #AI #Python

40 068

📌 Beyond Code Generation: AI for the Full Data Science Workflow 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-26 | ⏱️ Read time: 10 min read Using Codex and MCP to connect Google Drive, GitHub, BigQuery, and analysis in one real workflow #DataScience #AI #Python

40 068

📌 How to Make Your AI App Faster and More Interactive with Response Streaming 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-26 | ⏱️ Read time: 8 min read In my latest posts, we’ve talked a lot about prompt caching as well as caching… #DataScience #AI #Python

40 068

📌 My Models Failed. That’s How I Became a Better Data Scientist. 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-25 | ⏱️ Read time: 9 min read Data Leakage, Real-World Models, and the Path to Production AI in Healthcare #DataScience #AI #Python

40 068

📌 Building Human-In-The-Loop Agentic Workflows 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-25 | ⏱️ Read time: 10 min read Understanding how to set up human-in-the-loop (HITL) agentic workflows in LangGraph #DataScience #AI #Python

40 068

📌 The Machine Learning Lessons I’ve Learned This Month 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-03-25 | ⏱️ Read time: 5 min read Proactivity, blocking, and planning #DataScience #AI #Python

40 068

📌 Following Up on Like-for-Like for Stores: Handling PY 🗂 Category: DATA ANALYSIS 🕒 Date: 2026-03-25 | ⏱️ Read time: 7 min read My last article was about implementing Like-for-Like (L4L) for Stores. After discussing my solution with… #DataScience #AI #Python

40 068

📌 The Complete Guide to AI Implementation for Chief Data & AI Officers in 2026 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-24 | ⏱️ Read time: 29 min read How to leverage a framework to effectively prioritize AI Initiatives to rapidly accelerate growth and… #DataScience #AI #Python

40 068

📌 Production-Ready LLM Agents: A Comprehensive Framework for Offline Evaluation 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-24 | ⏱️ Read time: 18 min read We’ve become remarkably good at building sophisticated agent systems, but we haven’t developed the same… #DataScience #AI #Python

40 068

📌 From Dashboards to Decisions: Rethinking Data & Analytics in the Age of AI 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-24 | ⏱️ Read time: 7 min read How AI agents, data foundations, and human-centered analytics are reshaping the future of decision-making #DataScience #AI #Python

40 068

Repost from Machine Learning with Python

Visualizing the complexity of algorithms https://t.me/CodeProgrammer ✅