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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 40 208 subscribers, ranking 3 344 in the Technologies & Applications category and 228 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 40 208 subscribers.

According to the latest data from 03 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 338 over the last 30 days and by 9 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.04%. Within the first 24 hours after publication, content typically collects 2.42% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 822 views. Within the first day, a publication typically gains 973 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 04 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.

40 208
Subscribers
+924 hours
+727 days
+33830 days
Posts Archive
πŸ“Œ Understanding KL Divergence, Entropy, and Related Concepts πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-08 | ⏱️ Read time: 8
πŸ“Œ Understanding KL Divergence, Entropy, and Related Concepts πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-08 | ⏱️ Read time: 8 min read Important concepts in information theory, machine learning, and statistics

πŸ“Œ Nine Rules for Running Rust in the Browser πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2024-10-08 | ⏱️ Read time: 25 min read Practi
πŸ“Œ Nine Rules for Running Rust in the Browser πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2024-10-08 | ⏱️ Read time: 25 min read Practical lessons from porting range-set-blaze to WASM

πŸ“Œ Graph Neural Networks Part 2. Graph Attention Networks vs. GCNs πŸ—‚ Category: πŸ•’ Date: 2024-10-08 | ⏱️ Read time: 9 min rea
πŸ“Œ Graph Neural Networks Part 2. Graph Attention Networks vs. GCNs πŸ—‚ Category: πŸ•’ Date: 2024-10-08 | ⏱️ Read time: 9 min read A model that pays attention to your graph

πŸ“Œ Still Manually Reviewing All User Interactions For Your AI Solutions? πŸ—‚ Category: BUSINESS πŸ•’ Date: 2024-10-08 | ⏱️ Read
πŸ“Œ Still Manually Reviewing All User Interactions For Your AI Solutions? πŸ—‚ Category: BUSINESS πŸ•’ Date: 2024-10-08 | ⏱️ Read time: 7 min read Discover how to use cosine similarity to save hours and streamline your AI systems

πŸ“Œ TDS Newsletter: To Better Understand AI, Look Under the Hood πŸ—‚ Category: THE VARIABLE πŸ•’ Date: 2025-09-25 | ⏱️ Read time:
πŸ“Œ TDS Newsletter: To Better Understand AI, Look Under the Hood πŸ—‚ Category: THE VARIABLE πŸ•’ Date: 2025-09-25 | ⏱️ Read time: 3 min read AI-powered tools tend to generate extreme reactions: on one side we have the β€œIt’s magic!” and…

πŸ“Œ Make the Switch from Software Engineer to ML Engineer πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2024-10-08 | ⏱️ Read time: 9 min
πŸ“Œ Make the Switch from Software Engineer to ML Engineer πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2024-10-08 | ⏱️ Read time: 9 min read 7 steps that helped me transition from a software engineer to Machine Learning engineer

πŸ“Œ How to Improve Model Quality Without Building Larger Models πŸ—‚ Category: πŸ•’ Date: 2024-10-08 | ⏱️ Read time: 12 min read G
πŸ“Œ How to Improve Model Quality Without Building Larger Models πŸ—‚ Category: πŸ•’ Date: 2024-10-08 | ⏱️ Read time: 12 min read Going into the Google DeepMind’s β€œScaling LLM Test-Time Compute Optimally can be More Effective than…

πŸ“Œ A Deeper Dive into Odds Ratios Using Logistic Regression πŸ—‚ Category: STATISTICS πŸ•’ Date: 2024-10-08 | ⏱️ Read time: 21 mi
πŸ“Œ A Deeper Dive into Odds Ratios Using Logistic Regression πŸ—‚ Category: STATISTICS πŸ•’ Date: 2024-10-08 | ⏱️ Read time: 21 min read A comprehensive guide on how to extract and explore odds ratios from a Logistic Regression…

πŸ“Œ From Set Transformer to Perceiver Sampler πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-10-08 | ⏱️ Read time: 4 min read On mul
πŸ“Œ From Set Transformer to Perceiver Sampler πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-10-08 | ⏱️ Read time: 4 min read On multi-modal LLM Flamingo’s vision encoder

πŸ“Œ ITT vs LATE: Estimating Causal Effects with IV in Experiments with Imperfect Compliance πŸ—‚ Category: DATA SCIENCE πŸ•’ Date:
πŸ“Œ ITT vs LATE: Estimating Causal Effects with IV in Experiments with Imperfect Compliance πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-09 | ⏱️ Read time: 11 min read Intuition, step-by-step script, and assumptions needed for the use of IV

πŸ“Œ Embracing Uncertainty: The Power of Fuzzy Logic in Decision-Making πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-10-0
πŸ“Œ Embracing Uncertainty: The Power of Fuzzy Logic in Decision-Making πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-10-09 | ⏱️ Read time: 13 min read Exploring how fuzzy logic enhances AI, systems thinking, and real-world applications

πŸ“Œ 5 AI Projects You Can Build This Weekend (with Python) πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-09 | ⏱️ Read time: 8 min
πŸ“Œ 5 AI Projects You Can Build This Weekend (with Python) πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-09 | ⏱️ Read time: 8 min read From beginner-friendly to advanced

πŸ“Œ From Newton to LLM’s πŸ—‚ Category: PHYSICS πŸ•’ Date: 2024-10-09 | ⏱️ Read time: 17 min read A new approach to AI reasoning o
πŸ“Œ From Newton to LLM’s πŸ—‚ Category: PHYSICS πŸ•’ Date: 2024-10-09 | ⏱️ Read time: 17 min read A new approach to AI reasoning optimization

πŸ“Œ Mathematics I Look for in Data Scientist Interviews πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-09 | ⏱️ Read time: 18 min r
πŸ“Œ Mathematics I Look for in Data Scientist Interviews πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-09 | ⏱️ Read time: 18 min read Let’s rebuild our data science foundation.

πŸ“Œ Keep the Gradients Flowing πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-10-09 | ⏱️ Read time: 27 min read Optimizing
πŸ“Œ Keep the Gradients Flowing πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-10-09 | ⏱️ Read time: 27 min read Optimizing Sparse Neural Networks: Understanding Gradient Flow for Faster Training, and Better Performance in Deep…

πŸ“Œ Mastering Sample Size Calculations πŸ—‚ Category: πŸ•’ Date: 2024-10-09 | ⏱️ Read time: 19 min read A/B Testing, Reject Infere
πŸ“Œ Mastering Sample Size Calculations πŸ—‚ Category: πŸ•’ Date: 2024-10-09 | ⏱️ Read time: 19 min read A/B Testing, Reject Inference, and How to Get the Right Sample Size for Your Experiments

πŸ“Œ The Easiest Way to Learn and Use Python Today πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-10-09 | ⏱️ Read time: 9 m
πŸ“Œ The Easiest Way to Learn and Use Python Today πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-10-09 | ⏱️ Read time: 9 min read Google Colab and its integrated Generative AI, a powerful combination

πŸ“Œ The Most Valuable LLM Dev Skill is Easy to Learn, But Costly to Practice. πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-09 |
πŸ“Œ The Most Valuable LLM Dev Skill is Easy to Learn, But Costly to Practice. πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-09 | ⏱️ Read time: 18 min read Here’s how not to waste your budget on evaluating models and systems.

πŸ“Œ Fine-Tune Llama 3.2 for Powerful Performance on Targeted Tasks πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-10-10 | ⏱️ Read
πŸ“Œ Fine-Tune Llama 3.2 for Powerful Performance on Targeted Tasks πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-10-10 | ⏱️ Read time: 13 min read Learn how you can fine-tune Llama3.2, Meta’s most recent Large language model, to achieve powerful…

πŸ“Œ Forecasting with NHiTs: Uniting Deep Learning + Signal Processing Theory for Superior Accuracy πŸ—‚ Category: ARTIFICIAL INT
πŸ“Œ Forecasting with NHiTs: Uniting Deep Learning + Signal Processing Theory for Superior Accuracy πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-10-10 | ⏱️ Read time: 12 min read A high-performance DL model for all forecasting cases