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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 40 140 subscribers, ranking 3 371 in the Technologies & Applications category and 230 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 40 140 subscribers.

According to the latest data from 26 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 429 over the last 30 days and by 20 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 1.83%. Within the first 24 hours after publication, content typically collects 1.60% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 735 views. Within the first day, a publication typically gains 643 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
  • 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 27 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.

40 140
Subscribers
+2024 hours
+1017 days
+42930 days
Posts Archive
πŸ“Œ How to Automate Workflows with AI πŸ—‚ Category: AGENTIC AI πŸ•’ Date: 2025-11-15 | ⏱️ Read time: 7 min read Unlock the power
πŸ“Œ How to Automate Workflows with AI πŸ—‚ Category: AGENTIC AI πŸ•’ Date: 2025-11-15 | ⏱️ Read time: 7 min read Unlock the power of AI to streamline your operations. This guide details how to transform tedious manual processes into intelligent, automated workflows. Learn to identify key opportunities, select the right tools, and implement effective solutions to boost efficiency, reduce errors, and drive business innovation. #AI #WorkflowAutomation #ProcessOptimization

πŸ† Crack ML System Design Interviews πŸ“’ Crack ML System Design interviews for top tech roles! Learn to build and deploy large-scale intelligent systems, mastering high-stakes technical assessments at leading companies. ⚑ Tap to unlock the complete answer and gain instant insight. ━━━━━━━━━━━━━━━ By: @DataScienceM ✨

πŸ“Œ How to Crack Machine Learning System-Design Interviews πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-11-14 | ⏱️ Read time: 1
πŸ“Œ How to Crack Machine Learning System-Design Interviews πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-11-14 | ⏱️ Read time: 15 min read Ace your machine learning system design interviews at top tech companies. This comprehensive guide provides a deep dive into the interview process at Meta, Apple, Reddit, Amazon, Google, and Snap, equipping you with the strategies needed to succeed in these high-stakes technical assessments. #MachineLearning #SystemDesign #TechInterview #AI

πŸ“Œ β€œThe success of an AI product depends on how intuitively users can interact with its capabilities” πŸ—‚ Category: ARTIFICIAL
πŸ“Œ β€œThe success of an AI product depends on how intuitively users can interact with its capabilities” πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-11-14 | ⏱️ Read time: 8 min read Expert Janna Lipenkova emphasizes that the success of AI products hinges on intuitive user interaction, not just technological power. A winning AI strategy focuses on user-centric design, where deep domain knowledge is crucial for translating complex AI capabilities into accessible and valuable tools. This approach ensures that the product is not only intelligent but also seamlessly usable, defining the future of human-AI collaboration. #AIUX #ProductManagement #AIStrategy #MachineLearning

πŸ“Œ Critical Mistakes Companies Make When Integrating AI/ML into Their Processes πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-1
πŸ“Œ Critical Mistakes Companies Make When Integrating AI/ML into Their Processes πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-11-14 | ⏱️ Read time: 11 min read Integrating AI/ML into business operations is a complex process where many companies falter. Based on insights from leading AI teams across various industries, this guide highlights the critical, yet common, mistakes organizations make during AI adoption. Learn to navigate pitfalls related to strategy, data quality, and implementation to ensure your machine learning initiatives succeed and deliver tangible business value, avoiding costly errors and maximizing your return on investment. #AIIntegration #MachineLearning #AIStrategy #TechLeadership

πŸ“Œ Music, Lyrics, and Agentic AI: Building a Smart Song Explainer using Python and OpenAI πŸ—‚ Category: LARGE LANGUAGE MODELS
πŸ“Œ Music, Lyrics, and Agentic AI: Building a Smart Song Explainer using Python and OpenAI πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-11-14 | ⏱️ Read time: 10 min read This is how to build an AI-powered Song Explainer using Python and OpenAI #DataScience #AI #Python

πŸ“Œ Spearman Correlation Coefficient for When Pearson Isn’t Enough πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-11-13 | ⏱️ Read tim
πŸ“Œ Spearman Correlation Coefficient for When Pearson Isn’t Enough πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-11-13 | ⏱️ Read time: 7 min read Not all relationships are linear, and that is where Spearman comes in. #DataScience #AI #Python

πŸ“Œ Organizing Code, Experiments, and Research for Kaggle Competitions πŸ—‚ Category: PROJECT MANAGEMENT πŸ•’ Date: 2025-11-13 | ⏱
πŸ“Œ Organizing Code, Experiments, and Research for Kaggle Competitions πŸ—‚ Category: PROJECT MANAGEMENT πŸ•’ Date: 2025-11-13 | ⏱️ Read time: 21 min read Winning a Kaggle medal requires a disciplined approach, not just a great model. This guide shares essential lessons and tips from a medalist on effectively organizing your code, tracking experiments, and structuring your research. Learn how to streamline your competitive data science workflow, avoid common pitfalls, and improve your chances of success. #Kaggle #DataScience #MachineLearning #MLOps

πŸ“Œ Robotics with Python: Q-Learning vs Actor-Critic vs Evolutionary Algorithms πŸ—‚ Category: Uncategorized πŸ•’ Date: 2025-11-13
πŸ“Œ Robotics with Python: Q-Learning vs Actor-Critic vs Evolutionary Algorithms πŸ—‚ Category: Uncategorized πŸ•’ Date: 2025-11-13 | ⏱️ Read time: 15 min read Explore the intersection of Python and robotics in this deep dive into reinforcement learning algorithms. The article compares the trade-offs, strengths, and weaknesses of Q-Learning, Actor-Critic, and Evolutionary Algorithms for robotic control tasks. Learn how to apply these concepts by building a custom 3D environment to train and test your own RL-powered robot, providing a practical understanding of which technique to choose for your specific application. #Python #Robotics #ReinforcementLearning #MachineLearning #AI

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πŸ“Œ LLMs Are Randomized Algorithms πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-11-13 | ⏱️ Read time: 18 min read A surpri
πŸ“Œ LLMs Are Randomized Algorithms πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-11-13 | ⏱️ Read time: 18 min read A surprising link has been drawn between modern Large Language Models and the 50-year-old field of randomized algorithms. This perspective reframes LLMs not just as complex neural networks, but as a practical application of established algorithmic theory. Viewing today's most advanced AI through this lens offers a novel framework for analyzing their probabilistic nature, behavior, and underlying operational principles, bridging the gap between cutting-edge AI and foundational computer science. #LLMs #AI #RandomizedAlgorithms #ComputerScience #MachineLearning

Eurasia 2025 Window Fair 15-18 November 2025 Istanbul Turkey TΓΌyap Fair Center Hall: 3 Stand: 308 A At this importent event y
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Eurasia 2025 Window Fair 15-18 November 2025 Istanbul Turkey TΓΌyap Fair Center Hall: 3 Stand: 308 A At this importent event y
Eurasia 2025 Window Fair 15-18 November 2025 Istanbul Turkey TΓΌyap Fair Center Hall: 3 Stand: 308 A At this importent event you will have the opportunity to feel the pulse of the industry Sponsored By WaybienAds

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Eurasia 2025 Window Fair 15-18 November 2025 Istanbul Turkey TΓΌyap Fair Center Hall: 3 Stand: 308 A At this importent event y
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πŸ“Œ Feature Detection, Part 2: Laplace & Gaussian Operators πŸ—‚ Category: COMPUTER VISION πŸ•’ Date: 2025-11-12 | ⏱️ Read time: 1
πŸ“Œ Feature Detection, Part 2: Laplace & Gaussian Operators πŸ—‚ Category: COMPUTER VISION πŸ•’ Date: 2025-11-12 | ⏱️ Read time: 12 min read Laplace meets Gaussian β€” the story of two operators in edge detection #DataScience #AI #Python

πŸ“Œ How to Evaluate Retrieval Quality in RAG Pipelines (Part 3): DCG@k and NDCG@k πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date:
πŸ“Œ How to Evaluate Retrieval Quality in RAG Pipelines (Part 3): DCG@k and NDCG@k πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-11-12 | ⏱️ Read time: 8 min read This final part of the series on RAG pipeline evaluation explores advanced metrics for assessing retrieval quality. Learn how to use Discounted Cumulative Gain (DCG@k) and Normalized Discounted Cumulative Gain (NDCG@k) to measure the relevance and ranking of retrieved documents, moving beyond simpler metrics for a more nuanced understanding of your system's performance. #RAG #EvaluationMetrics #LLM #InformationRetrieval #MLOps

πŸ“Œ The Ultimate Guide to Power BI Aggregations πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-11-12 | ⏱️ Read time: 10 min read Unlo
πŸ“Œ The Ultimate Guide to Power BI Aggregations πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-11-12 | ⏱️ Read time: 10 min read Unlock significant performance gains in your Power BI reports by mastering aggregations. This guide explains how to leverage this powerful feature to optimize query performance and enhance user experience when working with massive datasets, enabling faster, more responsive analytics. #PowerBI #DataModeling #BusinessIntelligence #BigData

πŸ“Œ Deploy Your AI Assistant to Monitor and Debug n8n Workflows Using Claude and MCP πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ D
πŸ“Œ Deploy Your AI Assistant to Monitor and Debug n8n Workflows Using Claude and MCP πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-11-12 | ⏱️ Read time: 19 min read Learn how to deploy an AI assistant powered by Claude and MCP to effectively monitor, analyze, and debug your n8n workflows. This innovative approach allows you to troubleshoot complex automations using natural language conversations, significantly streamlining your development and maintenance process. #n8n #ClaudeAI #WorkflowAutomation #AIAssistant #Debugging