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

40 085

📌 The Machine Learning Lessons I’ve Learned This Month 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-03-02 | ⏱️ Read time: 6 min read February 2026: exchange with others, documentation, and MLOps #DataScience #AI #Python

40 085

Repost from Machine Learning with Python

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40 085

📌 YOLOv3 Paper Walkthrough: Even Better, But Not That Much 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-02 | ⏱️ Read time: 24 min read A PyTorch implementation on the YOLOv3 architecture from scratch #DataScience #AI #Python

40 085

📌 Exciting Changes Are Coming to the TDS Author Payment Program 🗂 Category: WRITING 🕒 Date: 2026-03-02 | ⏱️ Read time: 2 min read Authors can now benefit from updated earning tiers and a higher article cap #DataScience #AI #Python

40 085

📌 Context Engineering as Your Competitive Edge 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-03-01 | ⏱️ Read time: 13 min read If you have both unique domain expertise and know how to make it usable to… #DataScience #AI #Python

40 085

📌 Zero-Waste Agentic RAG: Designing Caching Architectures to Minimize Latency and LLM Costs at Scale 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-03-01 | ⏱️ Read time: 19 min read Reducing LLM costs by 30% with validation-aware, multi-tier caching #DataScience #AI #Python

40 085

📌 Scaling ML Inference on Databricks: Liquid or Partitioned? Salted or Not? 🗂 Category: DATA ENGINEERING 🕒 Date: 2026-02-28 | ⏱️ Read time: 11 min read A case study on techniques to maximize your clusters #DataScience #AI #Python

40 085

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40 085

📌 Claude Skills and Subagents: Escaping the Prompt Engineering Hamster Wheel 🗂 Category: AGENTIC AI 🕒 Date: 2026-02-28 | ⏱️ Read time: 17 min read How reusable, lazy-loaded instructions solve the context bloat problem in AI-assisted development. #DataScience #AI #Python

40 085

📌 The Gap Between Junior and Senior Data Scientists Isn’t Code 🗂 Category: DATA SCIENCE 🕒 Date: 2026-02-27 | ⏱️ Read time: 6 min read Why my obsession with complex algorithms was actually holding my career back. #DataScience #AI #Python

40 085

📌 Generative AI, Discriminative Human 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-02-27 | ⏱️ Read time: 14 min read How to think critically about AI in an ocean of hype #DataScience #AI #Python

40 085

📌 Stop Asking if a Model Is Interpretable 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-02-27 | ⏱️ Read time: 6 min read Start asking what question the explanation should answer. #DataScience #AI #Python

40 085

📌 Coding the Pong Game from Scratch in Python 🗂 Category: PROGRAMMING 🕒 Date: 2026-02-27 | ⏱️ Read time: 18 min read Implementing the classic Pong game in Python using OOP and Turtle #DataScience #AI #Python

40 085

📌 Take a Deep Dive into Filtering in DAX 🗂 Category: DATA ANALYSIS 🕒 Date: 2026-02-26 | ⏱️ Read time: 13 min read Have you ever wondered what happens when you apply a filter in a DAX expression?… #DataScience #AI #Python

40 085

📌 Designing Data and AI Systems That Hold Up in Production 🗂 Category: AUTHOR SPOTLIGHTS 🕒 Date: 2026-02-26 | ⏱️ Read time: 6 min read A system-level perspective on architecture, agents, and responsible scale #DataScience #AI #Python

40 085

Repost from Machine Learning with Python

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40 085

📌 Detecting and Editing Visual Objects with Gemini 🗂 Category: LLM APPLICATIONS 🕒 Date: 2026-02-26 | ⏱️ Read time: 34 min read A practical guide to identifying, restoring, and transforming elements within your images #DataScience #AI #Python

40 085

📌 A Generalizable MARL-LP Approach for Scheduling in Logistics 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-02-26 | ⏱️ Read time: 17 min read Part 1. Hybrid Solution for Dynamic Vehicle Routing — Context and Architecture #DataScience #AI #Python

40 085

📌 Breaking the Host Memory Bottleneck: How Peer Direct Transformed Gaudi’s Cloud Performance 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-02-25 | ⏱️ Read time: 9 min read Engineering RDMA-like performance over cloud host NICs using libfabric, DMA-BUF, and HCCL to restore distributed… #DataScience #AI #Python

40 085

📌 Scaling Feature Engineering Pipelines with Feast and Ray 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-02-25 | ⏱️ Read time: 11 min read Utilizing feature stores like Feast and distributed compute frameworks like Ray in production machine learning systems #DataScience #AI #Python