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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 40 255 subscribers, ranking 3 343 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 40 255 subscribers.

According to the latest data from 06 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 336 over the last 30 days and by -4 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.25%. Within the first 24 hours after publication, content typically collects 1.88% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 906 views. Within the first day, a publication typically gains 758 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 07 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.

40 255
Subscribers
-424 hours
+917 days
+33630 days
Posts Archive
πŸ“Œ Deep Dive into LlamaIndex Workflow: Event-Driven LLM Architecture πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-17 | ⏱️ Read
πŸ“Œ Deep Dive into LlamaIndex Workflow: Event-Driven LLM Architecture πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-17 | ⏱️ Read time: 17 min read What I think about the progress and shortcomings after practice

πŸ“Œ When Averages Lie: Moving Beyond Single-Point Predictions πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-17 | ⏱️ Read time: 18
πŸ“Œ When Averages Lie: Moving Beyond Single-Point Predictions πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-17 | ⏱️ Read time: 18 min read The Case for Predicting Full Probability Distributions in Decision-Making

πŸ“Œ Epic β€œCrossover” Between AlphaFold 3 and GPT-4o’s Knowledge of Protein Data Bank Entries πŸ—‚ Category: ARTIFICIAL INTELLIGE
πŸ“Œ Epic β€œCrossover” Between AlphaFold 3 and GPT-4o’s Knowledge of Protein Data Bank Entries πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-17 | ⏱️ Read time: 15 min read Exploring how GPT-4o’s knowledge of the Protein Data Bank coupled to systems like AlphaFold 3…

πŸ“Œ Four Career-Savers Data Scientists Should Incorporate into Their Work πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-1
πŸ“Œ Four Career-Savers Data Scientists Should Incorporate into Their Work πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-17 | ⏱️ Read time: 7 min read You might damage your data science career progress without even realising it – but avoiding…

πŸ“Œ The Invisible Bug That Broke My Automation: How OCR Changed The Game πŸ—‚ Category: πŸ•’ Date: 2024-12-17 | ⏱️ Read time: 9 mi
πŸ“Œ The Invisible Bug That Broke My Automation: How OCR Changed The Game πŸ—‚ Category: πŸ•’ Date: 2024-12-17 | ⏱️ Read time: 9 min read The evolution of AI in test automation: from locators to generative AI (Part 3)

πŸ“Œ 2024 in Review: What I Got Right, Where I Was Wrong, and Bolder Predictions for 2025 πŸ—‚ Category: ARTIFICIAL INTELLIGENCE
πŸ“Œ 2024 in Review: What I Got Right, Where I Was Wrong, and Bolder Predictions for 2025 πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-17 | ⏱️ Read time: 9 min read What I got right (and wrong) about trends in 2024 and daring to make bolder…

πŸ“Œ Will Your Christmas Be White? Ask An AI Weather Model! πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-17 | ⏱️ Read
πŸ“Œ Will Your Christmas Be White? Ask An AI Weather Model! πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-17 | ⏱️ Read time: 6 min read Learn how to visualize AI weather and create your own forecast for the holidays

πŸ“Œ Linear Optimisations in Product Analytics πŸ—‚ Category: ANALYTICS πŸ•’ Date: 2024-12-18 | ⏱️ Read time: 12 min read Solving t
πŸ“Œ Linear Optimisations in Product Analytics πŸ—‚ Category: ANALYTICS πŸ•’ Date: 2024-12-18 | ⏱️ Read time: 12 min read Solving the knapsack problem

πŸ“Œ Roadmap to Becoming a Data Scientist, Part 2: Software Engineering πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-18 | ⏱️ Read
πŸ“Œ Roadmap to Becoming a Data Scientist, Part 2: Software Engineering πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-18 | ⏱️ Read time: 14 min read Coding your road to Data Science: mastering key development skills

πŸ“Œ 100 Years of (eXplainable) AI πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-18 | ⏱️ Read time: 25 min read Reflect
πŸ“Œ 100 Years of (eXplainable) AI πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-18 | ⏱️ Read time: 25 min read Reflecting on advances and challenges in deep learning and explainability in the ever-evolving era of…

πŸ“Œ The Algorithm That Made Google Google πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-18 | ⏱️ Read time: 20 min read
πŸ“Œ The Algorithm That Made Google Google πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-18 | ⏱️ Read time: 20 min read How PageRank transformed how we searched the internet, and why it’s still playing an important…

πŸ“Œ Classifier-Free Guidance in LLMs Safety – NeurIPS 2024 Challenge Experience πŸ—‚ Category: πŸ•’ Date: 2024-12-18 | ⏱️ Read tim
πŸ“Œ Classifier-Free Guidance in LLMs Safety – NeurIPS 2024 Challenge Experience πŸ—‚ Category: πŸ•’ Date: 2024-12-18 | ⏱️ Read time: 7 min read LLM unlearning without model degradation is achieved through direct training on the replacement data and…

πŸ“Œ Introduction to TensorFlow’s Functional API πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-12-18 | ⏱️ Read time: 6 min read L
πŸ“Œ Introduction to TensorFlow’s Functional API πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-12-18 | ⏱️ Read time: 6 min read Learn what the Functional API is, and how to build complex keras models using it

πŸ“Œ Awesome Plotly with Code Series (Part 6): Dealing with Long Axis Labels πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-19 | ⏱️
πŸ“Œ Awesome Plotly with Code Series (Part 6): Dealing with Long Axis Labels πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-19 | ⏱️ Read time: 10 min read To rotate or not rotate? To truncate or to not truncate?

πŸ“Œ 2024 Highlights: The AI and Data Science Articles That Made a Splash πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-19 | ⏱️ Re
πŸ“Œ 2024 Highlights: The AI and Data Science Articles That Made a Splash πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-19 | ⏱️ Read time: 7 min read The stories that resonated the most with our community in the past year

πŸ“Œ Why Sets Are So Useful in Programming πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-19 | ⏱️ Read time: 8 min read And how you
πŸ“Œ Why Sets Are So Useful in Programming πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-19 | ⏱️ Read time: 8 min read And how you can use them to boost your code performance

πŸ“Œ Synthetic Control Sample for Before and After A/B Test πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-19 | ⏱️ Read time: 11 mi
πŸ“Œ Synthetic Control Sample for Before and After A/B Test πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-19 | ⏱️ Read time: 11 min read Learn a simple way to use linear regression to create a synthetic control sample for…

πŸ“Œ From Prototype to Production: Enhancing LLM Accuracy πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-19 | ⏱️ Read ti
πŸ“Œ From Prototype to Production: Enhancing LLM Accuracy πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-19 | ⏱️ Read time: 23 min read Implementing evaluation frameworks to optimize accuracy in real-world applications

πŸ“Œ Introducing Layer Enhanced Classification (LEC) πŸ—‚ Category: πŸ•’ Date: 2024-12-20 | ⏱️ Read time: 13 min read A novel appro
πŸ“Œ Introducing Layer Enhanced Classification (LEC) πŸ—‚ Category: πŸ•’ Date: 2024-12-20 | ⏱️ Read time: 13 min read A novel approach for lightweight safety classification using pruned language models

πŸ“Œ Semantically Compress Text to Save On LLM Costs πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-12-20 | ⏱️ Read time: 9 m
πŸ“Œ Semantically Compress Text to Save On LLM Costs πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-12-20 | ⏱️ Read time: 9 min read LLMs are great… if they can fit all of your data.