en
Feedback
Machine Learning

Machine Learning

Open in Telegram

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

Show more

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 40 191 subscribers, ranking 3 381 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 40 191 subscribers.

According to the latest data from 01 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 355 over the last 30 days and by 21 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 2.12% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 818 views. Within the first day, a publication typically gains 851 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 02 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.

40 191
Subscribers
+2124 hours
+857 days
+35530 days
Posts Archive
πŸ“Œ Visualising Strava Race Analysis πŸ—‚ Category: πŸ•’ Date: 2024-08-06 | ⏱️ Read time: 17 min read Two New Graphs That Compare
πŸ“Œ Visualising Strava Race Analysis πŸ—‚ Category: πŸ•’ Date: 2024-08-06 | ⏱️ Read time: 17 min read Two New Graphs That Compare Runners on the Same Event

πŸ“Œ Create Synthetic Dataset Using Llama 3.1 to Fine-Tune Your LLM πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-07 | ⏱️ Read tim
πŸ“Œ Create Synthetic Dataset Using Llama 3.1 to Fine-Tune Your LLM πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-07 | ⏱️ Read time: 10 min read Using the giant Llama 3.1 405B and Nvidia Nemotron 4 reward model to create a…

πŸ“Œ Stop Wasting LLM Tokens πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-07 | ⏱️ Read time: 5 min read Batching your inputs toge
πŸ“Œ Stop Wasting LLM Tokens πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-07 | ⏱️ Read time: 5 min read Batching your inputs together can lead to substantial savings without compromising on performance

πŸ“Œ Strategizing Your Preparation for Machine Learning Interviews πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2024-08-07 | ⏱️ Read tim
πŸ“Œ Strategizing Your Preparation for Machine Learning Interviews πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2024-08-07 | ⏱️ Read time: 10 min read Decoding Job Roles and identify focus areas

πŸ“Œ High-Performance Data Processing: pandas 2 vs. Polars, a vCPU Perspective πŸ—‚ Category: πŸ•’ Date: 2024-08-07 | ⏱️ Read time:
πŸ“Œ High-Performance Data Processing: pandas 2 vs. Polars, a vCPU Perspective πŸ—‚ Category: πŸ•’ Date: 2024-08-07 | ⏱️ Read time: 8 min read Polars promises its multithreading capabilities outperform pandas. But is it also the case with a…

πŸ“Œ Short and Sweet: Enhancing LLM Performance with Constrained Chain-of-Thought πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date:
πŸ“Œ Short and Sweet: Enhancing LLM Performance with Constrained Chain-of-Thought πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-07 | ⏱️ Read time: 10 min read Sometimes few words are enough: reducing output length for increasing accuracy

πŸ“Œ AI Shapeshifters: The Changing Role of the AI Engineer and Applied Data Scientist πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-
πŸ“Œ AI Shapeshifters: The Changing Role of the AI Engineer and Applied Data Scientist πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-07 | ⏱️ Read time: 5 min read The role of AI Engineer and Applied Data Scientist has undergone a remarkable transformation. Where…

πŸ“Œ Reinforcement Learning, Part 6: n-step Bootstrapping πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-07 | ⏱️ Read ti
πŸ“Œ Reinforcement Learning, Part 6: n-step Bootstrapping πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-07 | ⏱️ Read time: 7 min read Pushing the boundaries: generalizing temporal difference algorithms

πŸ“Œ Spatial Interpolation in Python πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-08 | ⏱️ Read time: 4 min read Using the Inverse
πŸ“Œ Spatial Interpolation in Python πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-08 | ⏱️ Read time: 4 min read Using the Inverse Distance Weighting method to infer missing spatial data

πŸ“Œ How to Use Machine Learning to Inform Design Decisions and Make Predictions πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-08
πŸ“Œ How to Use Machine Learning to Inform Design Decisions and Make Predictions πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-08 | ⏱️ Read time: 15 min read An Introductory Guide and Use Case for Applied Data Science

πŸ“Œ 5 Proven Query Translation Techniques To Boost Your RAG Performance πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-
πŸ“Œ 5 Proven Query Translation Techniques To Boost Your RAG Performance πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-08 | ⏱️ Read time: 11 min read How to get near-perfect LLM performance even with ambiguous user inputs

πŸ“Œ The Big Questions Shaping AI Today πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-08 | ⏱️ Read time: 4 min read Our
πŸ“Œ The Big Questions Shaping AI Today πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-08 | ⏱️ Read time: 4 min read Our weekly selection of must-read Editors’ Picks and original features

πŸ“Œ 3 Key Tweaks That Will Make Your Matplotlib Charts Publication Ready πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-08 | ⏱️ Re
πŸ“Œ 3 Key Tweaks That Will Make Your Matplotlib Charts Publication Ready πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-08 | ⏱️ Read time: 4 min read Matplotlib charts are an eyesore by default – here’s what to do about it.

πŸ“Œ Ask Not What AI Can Do for You – Ask What You Can Achieve with AI πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-08
πŸ“Œ Ask Not What AI Can Do for You – Ask What You Can Achieve with AI πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-08 | ⏱️ Read time: 11 min read Unlock AI for Everyone: Discover How You Can Use LLMs in Everyday Tasks

πŸ“Œ Create Stronger Decision Trees with bootstrapping and genetic algorithms πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 202
πŸ“Œ Create Stronger Decision Trees with bootstrapping and genetic algorithms πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-09 | ⏱️ Read time: 31 min read A technique to better allow decision trees to be used as interpretable models

πŸ“Œ We Need to Raise the Bar for AI Product Managers πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-09 | ⏱️ Read time:
πŸ“Œ We Need to Raise the Bar for AI Product Managers πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-09 | ⏱️ Read time: 10 min read How to Stop Blaming the β€˜Model’ and Start Building Successful AI Products

πŸ“Œ LLMOps – Serve a Llama-3 model with BentoML πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-09 | ⏱️ Read time: 5 min
πŸ“Œ LLMOps – Serve a Llama-3 model with BentoML πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-09 | ⏱️ Read time: 5 min read Quickly set up LLM APIs with BentoML and Runpod

πŸ“Œ AI for the Absolute Novice – Intuitively and Exhaustively Explained πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-
πŸ“Œ AI for the Absolute Novice – Intuitively and Exhaustively Explained πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-09 | ⏱️ Read time: 40 min read From β€œI’ve never coded” to making an AI model from scratch.

πŸ“Œ KernelSHAP can be misleading with correlated predictors πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-09 | ⏱️ Read
πŸ“Œ KernelSHAP can be misleading with correlated predictors πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-09 | ⏱️ Read time: 7 min read A concrete case study

πŸ“Œ Pre-Commit & Git Hooks: Automate High Code Quality πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-09 | ⏱️ Read time
πŸ“Œ Pre-Commit & Git Hooks: Automate High Code Quality πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-09 | ⏱️ Read time: 6 min read How to improve your code quality with pre-commit and git hooks