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 406...

📈 Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 040 subscribers, ranking 3 406 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 40 040 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.

40 040

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

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📌 Lasso Regression: Why the Solution Lives on a Diamond 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-04-23 | ⏱️ Read time: 24 min read It’s simpler than you think. #DataScience #AI #Python

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📌 Your Synthetic Data Passed Every Test and Still Broke Your Model 🗂 Category: DATA SCIENCE 🕒 Date: 2026-04-23 | ⏱️ Read time: 11 min read The silent gaps in synthetic data that only show up when your model is already… #DataScience #AI #Python

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🧮 $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

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📌 I Simulated an International Supply Chain and Let OpenClaw Monitor It 🗂 Category: AGENTIC AI 🕒 Date: 2026-04-23 | ⏱️ Read time: 9 min read Mario asked me why 18% of his shipments were late when every team hit their… #DataScience #AI #Python

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A trusted platform for cryptocurrency enthusiasts and reliable trading.

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📌 Using a Local LLM as a Zero-Shot Classifier 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-04-23 | ⏱️ Read time: 8 min read A practical pipeline for classifying messy free-text data into meaningful categories using a locally hosted… #DataScience #AI #Python

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📌 How to Run OpenClaw with Open-Source Models 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-04-22 | ⏱️ Read time: 8 min read Run OpenClaw assistant through alternative LLMs #DataScience #AI #Python

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Today, the public mint for Lobsters on TON goes live on Getgems 🦞 This is not just another NFT drop. In my view, Lobsters is one of the first truly cohesive products at the intersection of blockchain, NFTs, and AI. Here, the NFT is not just an image and not just a collectible. Each Lobster is an NFT with a built-in AI agent inside: a digital character with its own soul, on-chain biography, persistent memory, and a unified identity across Telegram, Mini App, Claude, and API. So you are not just getting an asset in your wallet. You are getting an AI-native digital character that can interact, remember, and stay consistent across different interfaces. What makes this especially interesting is the timing. In the recent video Pavel Durov shared in his post about agentic bots in Telegram, the lobster imagery was right there. Against that backdrop, Lobsters does not feel like a random mint — it feels like a very precise fit for the new narrative: Telegram-native agents + TON infrastructure + NFT ownership layer + AI utility Put simply, this is one of the first real attempts to turn an NFT from “just an image” into a digital agent. Public mint: today, 16:00 Price: 50 TON 👉 Mint your Lobster on Getgems 🦞🦞🦞

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📌 Ivory Tower Notes: The Methodology 🗂 Category: DATA SCIENCE 🕒 Date: 2026-04-22 | ⏱️ Read time: 6 min read A short intro to scientific methodology to combat “prompt in, slop out” #DataScience #AI #Python

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📌 From Ad Hoc Prompting to Repeatable AI Workflows with Claude Code Skills 🗂 Category: AGENTIC AI 🕒 Date: 2026-04-22 | ⏱️ Read time: 8 min read How I turned LLM persona interviews into a repeatable customer research workflow #DataScience #AI #Python

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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

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📌 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

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📌 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

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📌 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

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📌 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

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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.