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Machine Learning

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

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📈 Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 323 subscribers, ranking 3 332 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 40 323 subscribers.

According to the latest data from 09 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 378 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 2.23%. 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 897 views. Within the first day, a publication typically gains 788 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 10 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.

40 323
Subscribers
+3024 hours
+1067 days
+37830 days
Posts Archive
📌 Unlock the Power of ROC Curves: Intuitive Insights for Better Model Evaluation 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-
📌 Unlock the Power of ROC Curves: Intuitive Insights for Better Model Evaluation 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-08 | ⏱️ Read time: 8 min read Go beyond the definitions: grasp the real meaning of AUC and ROC analysis for practical…

📌 A Data Scientist’s Guide to Docker Containers 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-08 | ⏱️ Read time: 11 min read Ho
📌 A Data Scientist’s Guide to Docker Containers 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-08 | ⏱️ Read time: 11 min read How to enable your ML model to run anywhere

📌 Mining Rules from Data 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-09 | ⏱️ Read time: 20 min read Using decision trees for
📌 Mining Rules from Data 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-09 | ⏱️ Read time: 20 min read Using decision trees for quick segmentation

📌 Time Series Forecasting Made Simple (Part 1): Decomposition and Baseline Models 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04
📌 Time Series Forecasting Made Simple (Part 1): Decomposition and Baseline Models 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-09 | ⏱️ Read time: 12 min read Learn the intuition behind time series decomposition, additive vs. multiplicative models and build your first…

📌 Why CatBoost Works So Well: The Engineering Behind the Magic 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-04-09 | ⏱️ Read t
📌 Why CatBoost Works So Well: The Engineering Behind the Magic 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-04-09 | ⏱️ Read time: 10 min read CatBoost stands out by directly tackling a long-standing challenge in gradient boosting—how to handle categorical…

📌 Deb8flow: Orchestrating Autonomous AI Debates with LangGraph and GPT-4o 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025
📌 Deb8flow: Orchestrating Autonomous AI Debates with LangGraph and GPT-4o 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-04-10 | ⏱️ Read time: 29 min read Inside Deb8flow: Real-time AI debates with LangGraph and GPT-4o

📌 Ivory Tower Notes: The Problem 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-10 | ⏱️ Read time: 12 min read When a data scien
📌 Ivory Tower Notes: The Problem 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-10 | ⏱️ Read time: 12 min read When a data science problem is “the” problem

📌 How to Measure Real Model Accuracy When Labels Are Noisy 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-10 | ⏱️ Read time: 5 m
📌 How to Measure Real Model Accuracy When Labels Are Noisy 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-10 | ⏱️ Read time: 5 min read The math behind “true” accuracy and error correlation

📌 The Invisible Revolution: How Vectors Are (Re)defining Business Success 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-10 | ⏱️
📌 The Invisible Revolution: How Vectors Are (Re)defining Business Success 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-10 | ⏱️ Read time: 26 min read The hidden force behind AI is powering the next wave of business transformation

📌 The What, How, and Why of Agentic AI 🗂 Category: THE VARIABLE 🕒 Date: 2025-04-10 | ⏱️ Read time: 3 min read This week, w
📌 The What, How, and Why of Agentic AI 🗂 Category: THE VARIABLE 🕒 Date: 2025-04-10 | ⏱️ Read time: 3 min read This week, we tackle the nitty-gritty details of working with agentic AI.

📌 The Basis of Cognitive Complexity: Teaching CNNs to See Connections 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-04-
📌 The Basis of Cognitive Complexity: Teaching CNNs to See Connections 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-04-11 | ⏱️ Read time: 9 min read Transforming CNNs: From task-specific learning to abstract generalization

📌 Are You Sure Your Posterior Makes Sense? 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-11 | ⏱️ Read time: 26 min read A detai
📌 Are You Sure Your Posterior Makes Sense? 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-11 | ⏱️ Read time: 26 min read A detailed guide on how to use diagnostics to evaluate the performance of MCMC samplers

📌 Learnings from a Machine Learning Engineer — Part 6: The Human Side 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-04-
📌 Learnings from a Machine Learning Engineer — Part 6: The Human Side 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-04-11 | ⏱️ Read time: 16 min read Practical advice for the humans involved with machine learning

📌 Sesame Speech Model: How This Viral AI Model Generates Human-Like Speech 🗂 Category: CONVERSATIONAL AI 🕒 Date: 2025-04-1
📌 Sesame  Speech Model:  How This Viral AI Model Generates Human-Like Speech 🗂 Category: CONVERSATIONAL AI 🕒 Date: 2025-04-11 | ⏱️ Read time: 9 min read A deep dive into residual vector quantizers, conversational speech AI, and talkative transformers.

📌 Layers of the AI Stack, Explained Simply 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-04-14 | ⏱️ Read time: 14 min r
📌 Layers of the AI Stack, Explained Simply 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-04-14 | ⏱️ Read time: 14 min read And why I decided to work at the application layer

📌 An LLM-Based Workflow for Automated Tabular Data Validation 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-14 | ⏱️ Read time:
📌 An LLM-Based Workflow for Automated Tabular Data Validation 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-14 | ⏱️ Read time: 12 min read Clean data, clear insights: detect and correct data quality issues without manual intervention.

📌 Plotly’s AI Tools Are Redefining Data Science Workflows 🗂 Category: SPONSORED CONTENT 🕒 Date: 2025-04-15 | ⏱️ Read time:
📌 Plotly’s AI Tools Are Redefining Data Science Workflows 🗂 Category: SPONSORED CONTENT 🕒 Date: 2025-04-15 | ⏱️ Read time: 8 min read How Plotly’s AI-powered tools are transforming data science workflows with faster development, smarter insights, and…

📌 An Unbiased Review of Snowflake’s Document AI 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-04-15 | ⏱️ Read time: 8 min
📌 An Unbiased Review of Snowflake’s Document AI 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-04-15 | ⏱️ Read time: 8 min read Or, how we spared a human from manually inspecting 10,000 flu shot documents.

📌 When Predictors Collide: Mastering VIF in Multicollinear Regression 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-16 | ⏱️ Rea
📌 When Predictors Collide: Mastering VIF in Multicollinear Regression 🗂 Category: DATA SCIENCE 🕒 Date: 2025-04-16 | ⏱️ Read time: 11 min read Explore how the Variance Inflation Factor helps detect and manage multicollinearity in your regression models.

📌 The Good-Enough Truth 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-04-17 | ⏱️ Read time: 7 min read Lies, damned lie
📌 The Good-Enough Truth 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-04-17 | ⏱️ Read time: 7 min read Lies, damned lies, and LLMs