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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 40 291 subscribers, ranking 3 341 in the Technologies & Applications category and 226 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 40 291 subscribers.

According to the latest data from 08 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 353 over the last 30 days and by 12 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.31%. Within the first 24 hours after publication, content typically collects 1.90% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 929 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 09 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.

40 291
Subscribers
+1224 hours
+867 days
+35330 days
Posts Archive
πŸ“Œ No Peeking Ahead: Time-Aware Graph Fraud Detection πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-09-14 | ⏱️ Read time: 15 mi
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πŸ“Œ Roadmap to Becoming a Data Scientist, Part 3: Machine Learning πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2025-01-14 | ⏱️ Read ti
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πŸ“Œ Using Optimization to Solve Adversarial Problems πŸ—‚ Category: πŸ•’ Date: 2025-01-14 | ⏱️ Read time: 41 min read An example o
πŸ“Œ Using Optimization to Solve Adversarial Problems πŸ—‚ Category: πŸ•’ Date: 2025-01-14 | ⏱️ Read time: 41 min read An example of simultaneously optimizing two policies for two adversarial agents, looking specifically at the…

πŸ“Œ You Think 80% Means 80%? Why Prediction Probabilities Need a Second Look πŸ—‚ Category: πŸ•’ Date: 2025-01-14 | ⏱️ Read time:
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πŸ“Œ From Darwin to Deep Work πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-14 | ⏱️ Read time: 7 min read Focus Strategies for Mac
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πŸ“Œ Awesome Plotly with Code Series (Part 8): How to Balance Dominant Bar Chart Categories πŸ—‚ Category: DATA SCIENCE πŸ•’ Date:
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πŸ“Œ Why Normalization Is Crucial for Policy Evaluation in Reinforcement Learning πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-0
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πŸ“Œ Scale Experiment Decision-Making with Programmatic Decision Rules πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-14 | ⏱️ Read
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πŸ“Œ How To: Forecast Time Series Using Lags πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-14 | ⏱️ Read time: 8 min read Lag colum
πŸ“Œ How To: Forecast Time Series Using Lags πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-14 | ⏱️ Read time: 8 min read Lag columns can significantly boost your model’s performance

πŸ“Œ Hands-On Delivery Routes Optimization (TSP) with AI, Using LKH and Python πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-14 |
πŸ“Œ Hands-On Delivery Routes Optimization (TSP) with AI, Using LKH and Python πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-14 | ⏱️ Read time: 12 min read Here’s how to optimize the delivery routes, from theory to code.

πŸ“Œ Basics of GANs & SMOTE for Data Augmentation πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-15 | ⏱️ Read time: 14 min read GAN
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πŸ“Œ Scaling Segmentation with Blender: How to Automate Dataset Creation πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-15 | ⏱️ Rea
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πŸ“Œ LossVal Explained: Efficiently Estimate the Importance of Your Training Data πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-15
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πŸ“Œ Recursive Walks down User Referral Trees πŸ—‚ Category: MARKETING πŸ•’ Date: 2025-01-15 | ⏱️ Read time: 6 min read Measuring t
πŸ“Œ Recursive Walks down User Referral Trees πŸ—‚ Category: MARKETING πŸ•’ Date: 2025-01-15 | ⏱️ Read time: 6 min read Measuring the total influence of users in a user referral program by traversing indirect referrals

πŸ“Œ Unlocking the Power of Machine Learning in Analytics: Practical Use Cases and Skills πŸ—‚ Category: ANALYTICS πŸ•’ Date: 2025-
πŸ“Œ Unlocking the Power of Machine Learning in Analytics: Practical Use Cases and Skills πŸ—‚ Category: ANALYTICS πŸ•’ Date: 2025-01-15 | ⏱️ Read time: 9 min read Your essential machine learning checklist to excel as a data scientist in analytics

πŸ“Œ Understanding Flash Attention: Writing the Algorithm from Scratch in Triton πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date:
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πŸ“Œ What Did I Learn from Building LLM Applications in 2024? – Part 2 πŸ—‚ Category: πŸ•’ Date: 2025-01-17 | ⏱️ Read time: 13 min
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πŸ“Œ Influential Time-Series Forecasting Papers of 2023-2024: Part 1 πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-01-17 |
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