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Machine Learning

Machine Learning

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Real Machine Learning β€” simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

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πŸ“ˆ Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 149 subscribers, ranking 3 375 in the Technologies & Applications category and 227 in the Syria region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 40 149 subscribers.

According to the latest data from 28 June, 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 7 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.09%. Within the first 24 hours after publication, content typically collects 1.91% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 841 views. Within the first day, a publication typically gains 766 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.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œReal Machine Learning β€” 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 29 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.

40 149
Subscribers
+724 hours
+1147 days
+37830 days
Posts Archive
πŸ“Œ Causal Inference with Python: A Guide to Propensity Score Matching πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-02 | ⏱️ Read
πŸ“Œ Causal Inference with Python: A Guide to Propensity Score Matching πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-02 | ⏱️ Read time: 28 min read An introduction to estimating treatment effects in non-randomized settings using practical examples and Python code

πŸ“Œ Eco-Friendly AI: How to Reduce the Carbon and Water Footprints of Your ML Models πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ D
πŸ“Œ Eco-Friendly AI: How to Reduce the Carbon and Water Footprints of Your ML Models πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-07-03 | ⏱️ Read time: 14 min read Sustainable practices for model training and serving

πŸ“Œ The Math Behind Risk – Part 2 πŸ—‚ Category: DATA VISUALIZATION πŸ•’ Date: 2024-07-03 | ⏱️ Read time: 11 min read Does the att
πŸ“Œ The Math Behind Risk – Part 2 πŸ—‚ Category: DATA VISUALIZATION πŸ•’ Date: 2024-07-03 | ⏱️ Read time: 11 min read Does the attack really have an advantage in the game of world conquest?

πŸ“Œ Not All HNSW Indices Are Made Equaly πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-07-03 | ⏱️ Read time: 8 min read O
πŸ“Œ Not All HNSW Indices Are Made Equaly πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-07-03 | ⏱️ Read time: 8 min read Overcoming Major HNSW Challenges to Improve the Efficiency of Your AI Production Workload

πŸ“Œ How to challenge your own analysis so others won’t πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-03 | ⏱️ Read time: 14 min re
πŸ“Œ How to challenge your own analysis so others won’t πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-03 | ⏱️ Read time: 14 min read Master the art of sanity checks to level up the quality of your work

πŸ“Œ A Comprehensive Guide to Collaborative AI Agents in Practice πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-07-03 | ⏱️ R
πŸ“Œ A Comprehensive Guide to Collaborative AI Agents in Practice πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-07-03 | ⏱️ Read time: 17 min read the definition, and building a team of agents that refine your CV and Cover Letter…

πŸ“Œ AutoML with AutoGluon: Transform Your ML Workflow with Just Four Lines of Code πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-07
πŸ“Œ AutoML with AutoGluon: Transform Your ML Workflow with Just Four Lines of Code πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-07-03 | ⏱️ Read time: 23 min read How AutoGluon Dominated Kaggle Competitions and How You Can Beat It. The algorithm that beats…

πŸ€–πŸ§  Quivr AI: Building Your Second Brain with Open-Source Generative Intelligence πŸ—“οΈ 12 Oct 2025 πŸ“š AI News & Trends In the
πŸ€–πŸ§  Quivr AI: Building Your Second Brain with Open-Source Generative Intelligence πŸ—“οΈ 12 Oct 2025 πŸ“š AI News & Trends In the rapidly evolving landscape of artificial intelligence, developers and businesses are seeking solutions that merge flexibility, power, and simplicity. Enter Quivr β€” an open-source framework designed to help you build your own β€œsecond brain” powered by Generative AI. Whether you’re an indie developer, startup founder or enterprise engineer, it makes it possible to integrate ... #QuivrAI #SecondBrain #GenerativeAI #OpenSourceAI #AIFramework #AIProductivity

πŸ“Œ OMOP & DataSHIELD: A perfect match to elevate privacy-enhancing healthcare analytics? πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2
πŸ“Œ OMOP & DataSHIELD: A perfect match to elevate privacy-enhancing healthcare analytics? πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-03 | ⏱️ Read time: 8 min read OMOP & DataSHIELD: A Perfect Match to Elevate Privacy-Enhancing Healthcare Analytics? Context Cross-border or multi-site…

πŸ“Œ Diffusion Model from Scratch in Pytorch πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-07-04 | ⏱️ Read time: 15 min read Impleme
πŸ“Œ Diffusion Model from Scratch in Pytorch πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-07-04 | ⏱️ Read time: 15 min read Implementation of Denoising Diffusion Probabilistic Models (DDPM)

πŸ“Œ From MOCO v1 to v3: Towards Building a Dynamic Dictionary for Self-Supervised Learning – Part 1 πŸ—‚ Category: DEEP LEARNING
πŸ“Œ From MOCO v1 to v3: Towards Building a Dynamic Dictionary for Self-Supervised Learning – Part 1 πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-07-04 | ⏱️ Read time: 7 min read A gentle recap on the momentum contrast learning framework

πŸ“Œ LLM Apps, Crucial Data Skills, Multi-Agent AI Systems, and Other June Must-Reads πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-0
πŸ“Œ LLM Apps, Crucial Data Skills, Multi-Agent AI Systems, and Other June Must-Reads πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-04 | ⏱️ Read time: 4 min read The stories that resonated the most with our community in the past month

πŸ€–πŸ§  Top 20 Ultimate Bollywood Diwali Portrait Ideas for Women Using Gemini AI πŸ—“οΈ 12 Oct 2025 πŸ“š AI News & Trends Diwali 202
πŸ€–πŸ§  Top 20 Ultimate Bollywood Diwali Portrait Ideas for Women Using Gemini AI πŸ—“οΈ 12 Oct 2025 πŸ“š AI News & Trends Diwali 2025 is around the corner, and celebrations are not just about lights and sweets anymore. With Gemini AI, you can now transform your selfies into cinematic, vintage Bollywood-style portraits that capture the nostalgic charm of the 90s. From glowing diyas to intricate lehengas and sarees, Gemini AI allows you to bring the festival of ... #BollywoodDiwali #GeminiAI #FestivalPortraits #Diwali2025 #AIImageGeneration #WomenFashion

πŸ“Œ How Should You Test Your Machine Learning Project? A Beginner’s Guide πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-04 | ⏱️ R
πŸ“Œ How Should You Test Your Machine Learning Project? A Beginner’s Guide πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-04 | ⏱️ Read time: 11 min read A friendly introduction to testing machine learning projects, by using standard libraries such as Pytest…

πŸ“Œ The Machine Learning Guide for Predictive Accuracy: Interpolation and Extrapolation πŸ—‚ Category: ARTIFICIAL INTELLIGENCE οΏ½
πŸ“Œ The Machine Learning Guide for Predictive Accuracy: Interpolation and Extrapolation πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-07-04 | ⏱️ Read time: 15 min read Evaluating machine learning models beyond training data

πŸ“Œ PySpark Explained: Four Ways to Create and Populate DataFrames πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-07-04 | ⏱️ Read
πŸ“Œ PySpark Explained: Four Ways to Create and Populate DataFrames πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-07-04 | ⏱️ Read time: 11 min read From CSVs to databases: loading data into PySpark DataFrames

πŸ“Œ Time Series Forecasting in the Age of GenAI: Make Gradient Boosting Behaves like LLMs πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2
πŸ“Œ Time Series Forecasting in the Age of GenAI: Make Gradient Boosting Behaves like LLMs πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-04 | ⏱️ Read time: 6 min read Applying zero-shot forecasting with standard machine learning models

πŸ€–πŸ§  Try Powerful Mem0 AI to build Long-Term Memory for AI Agents πŸ—“οΈ 12 Oct 2025 πŸ“š AI News & Trends Artificial Intelligence
πŸ€–πŸ§  Try Powerful Mem0 AI to build Long-Term Memory for AI Agents πŸ—“οΈ 12 Oct 2025 πŸ“š AI News & Trends Artificial Intelligence has made incredible leaps in recent years from chatbots that converse naturally to AI agents capable of reasoning and decision-making. However, one major limitation has persisted: memory. Traditional large language models (LLMs) like ChatGPT or Claude can process vast data but fail to remember context across long interactions. This is where Mem0 AI, ... #Mem0AI #AIAgents #LongTermMemory #ArtificialIntelligence #AIMemory #LLMs

πŸ“Œ LLM Alignment: Reward-Based vs Reward-Free Methods πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-07-05 | ⏱️ Read time: 12 mi
πŸ“Œ LLM Alignment: Reward-Based vs Reward-Free Methods πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-07-05 | ⏱️ Read time: 12 min read Optimization methods for LLM alignment

πŸ“Œ How Big Tech Is Exploiting Content Creators, And (Trying To) Get Away With It πŸ—‚ Category: BIG TECH πŸ•’ Date: 2024-07-05 |
πŸ“Œ How Big Tech Is Exploiting Content Creators, And (Trying To) Get Away With It πŸ—‚ Category: BIG TECH πŸ•’ Date: 2024-07-05 | ⏱️ Read time: 20 min read If you’re reading this, you’re part of the content creator ecosystem: either as a fellow…