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 402...

📈 Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 057 subscribers, ranking 3 402 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 40 057 subscribers.

According to the latest data from 22 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 372 over the last 30 days and by 2 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 1.94%. 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 775 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 23 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.

40 057

Subscribers

+224 hours

+237 days

+37230 days

775

Post views

~ 46624 hours

~ 62048 hours

1.94%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

40 069

11 Plots Data Scientists Use 90% of the Time 📊🚀 Here’s the secret → Data scientists don’t actually use 100+ types of charts. 🤫 When real decisions are on the line, it always comes back to the same 11. https://t.me/DataScienceM

40 069

📌 Correlation vs. Causation: Measuring True Impact with Propensity Score Matching 🗂 Category: DATA SCIENCE 🕒 Date: 2026-04-22 | ⏱️ Read time: 12 min read Learn how Propensity Score Matching uncovers true causality in observational data. By finding “statistical twins,”… #DataScience #AI #Python

40 069

📌 Using Causal Inference to Estimate the Impact of Tube Strikes on Cycling Usage in London 🗂 Category: DATA SCIENCE 🕒 Date: 2026-04-22 | ⏱️ Read time: 19 min read Turning free-to-use data into a hypothesis-ready dataset #DataScience #AI #Python

40 069

📌 Your RAG Gets Confidently Wrong as Memory Grows – I Built the Memory Layer That Stops It 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-04-21 | ⏱️ Read time: 15 min read As memory grows in RAG systems, accuracy quietly drops while confidence rises — creating a… #DataScience #AI #Python

40 069

🧮 $40/day × 30 days = $1,200/month. That's what my students average. From their phone. In 10 minutes a day. No degree needed. No investment knowledge required. Just Copy & Paste my moves. I'm Tania, and this is real. 👉 Join for Free, Click here #ad 📢 InsideAd

40 069

📌 I Replaced GPT-4 with a Local SLM and My CI/CD Pipeline Stopped Failing 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-04-21 | ⏱️ Read time: 13 min read The hidden cost of probabilistic outputs in systems that demand reliability #DataScience #AI #Python

40 069

Repost from Machine Learning with Python

🔥 Google Colab has added the option of retraining 500+ open-source neural networks Unsloth has released a convenient notebook for configuring models. Instructions: 1. Open the page in Colab: https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb 2. Run the blocks and the Unsloth Studio itself. 3. Select a model and a dataset. 4. Click "Start Training" and monitor the progress in real time. 5. Everything is ready - you can immediately compare the regular and fine-tuned versions of the model in the chat.

40 069

📌 How to Call Rust from Python 🗂 Category: PROGRAMMING 🕒 Date: 2026-04-21 | ⏱️ Read time: 10 min read A guide to bridging the gap between ease of use and raw performance. #DataScience #AI #Python

40 069

📌 Git UNDO : How to Rewrite Git History with Confidence 🗂 Category: PROGRAMMING 🕒 Date: 2026-04-21 | ⏱️ Read time: 24 min read For any data scientist who works in a team, being able to undo Git actions… #DataScience #AI #Python

40 069

📌 DIY AI & ML: Solving The Multi-Armed Bandit Problem with Thompson Sampling 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-04-21 | ⏱️ Read time: 17 min read How you can build your own Thompson Sampling Algorithm object in Python and apply it… #DataScience #AI #Python

40 069

📌 From Risk to Asset: Designing a Practical Data Strategy That Actually Works 🗂 Category: DATA SCIENCE 🕒 Date: 2026-04-20 | ⏱️ Read time: 11 min read How to turn data into a strategic asset that enables faster decisions, reduces uncertainty, and… #DataScience #AI #Python

40 069

Repost from Machine Learning with Python

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40 069

📌 The LLM Gamble 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-04-20 | ⏱️ Read time: 8 min read Why it tickles your brain to use an LLM, and what that means for the… #DataScience #AI #Python

40 069

📌 Context Payload Optimization for ICL-Based Tabular Foundation Models 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-04-20 | ⏱️ Read time: 16 min read Conceptual overview and practical guidance #DataScience #AI #Python

40 069

📌 What Does the p-value Even Mean? 🗂 Category: DATA SCIENCE 🕒 Date: 2026-04-20 | ⏱️ Read time: 7 min read And what does it tell us? #DataScience #AI #Python

40 069

📌 KV Cache Is Eating Your VRAM. Here’s How Google Fixed It With TurboQuant. 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-04-19 | ⏱️ Read time: 11 min read Explore the end-to-end pipeline of TurboQuant, a novel KV cache quantization framework. This overview breaks… #DataScience #AI #Python

40 069

📌 Dreaming in Cubes 🗂 Category: DEEP LEARNING 🕒 Date: 2026-04-19 | ⏱️ Read time: 10 min read Generating Minecraft Worlds with Vector Quantized Variational Autoencoders (VQ-VAE) and Transformers #DataScience #AI #Python

40 069

📌 Proxy-Pointer RAG: Structure Meets Scale at 100% Accuracy with Smarter Retrieval 🗂 Category: LARGE LANGUAGE MODEL 🕒 Date: 2026-04-19 | ⏱️ Read time: 14 min read Open source. 5-minute setup. Vector RAG done right—try it yourself. #DataScience #AI #Python

40 069

📌 Your RAG System Retrieves the Right Data — But Still Produces Wrong Answers. Here’s Why (and How to Fix It). 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-04-18 | ⏱️ Read time: 17 min read Your RAG system is retrieving the right documents with perfect scores — yet it still… #DataScience #AI #Python