Machine Learning

Open in Telegram

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

Network:Machine Learning with Python Syria227 Technologies & Applications3 364...

📈 Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 150 subscribers, ranking 3 364 in the Technologies & Applications category and 227 in the Syria region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 150 subscribers.

According to the latest data from 27 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 412 over the last 30 days and by 5 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 1.96%. Within the first 24 hours after publication, content typically collects 1.89% reactions from the total number of subscribers.
Post reach: On average, each post receives 785 views. Within the first day, a publication typically gains 760 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.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Real Machine Learning — 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 28 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.

40 150

Subscribers

+524 hours

+1067 days

+41230 days

785

Post views

~ 76024 hours

~ 1 06948 hours

1.96%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

40 146

🤖🧠 NVIDIA, MIT, HKU and Tsinghua University Introduce QeRL: A Powerful Quantum Leap in Reinforcement Learning for LLMs 🗓️ 17 Oct 2025 📚 AI News & Trends The rise of large language models (LLMs) has redefined artificial intelligence powering everything from conversational AI to autonomous reasoning systems. However, training these models especially through reinforcement learning (RL) is computationally expensive requiring massive GPU resources and long training cycles. To address this, a team of researchers from NVIDIA, Massachusetts Institute of Technology (MIT), The ... #QuantumLearning #ReinforcementLearning #LLMs #NVIDIA #MIT #TsinghuaUniversity

40 146

📌 How I Built an LLM-Based Game from Scratch 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-11 | ⏱️ Read time: 17 min read Part I: Game concepts and Causal Graphs for LLMs

40 146

📌 Optimize Production with R - Part I 🗂 Category: 🕒 Date: 2024-06-11 | ⏱️ Read time: 8 min read An introduction to linear programming with R

40 146

📌 Beyond FOMO – Keeping up to date in AI 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-11 | ⏱️ Read time: 9 min read Don’t get stressed but enjoy the journey.

40 146

📌 Multi-Head Attention – Formally Explained and Defined 🗂 Category: DEEP LEARNING 🕒 Date: 2024-06-11 | ⏱️ Read time: 10 min read A comprehensive and detailed formalization of multi-head attention.

40 146

📌 How to Maximize Your Impact as a Data Scientist 🗂 Category: ANALYTICS 🕒 Date: 2024-06-11 | ⏱️ Read time: 13 min read Actionable advice to accelerate your career

40 146

📌 Key Roles in a Fraud Prediction project with Machine Learning 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-06-11 | ⏱️ Read time: 6 min read What type of roles are involved in developing a ML model for fraud prediction?

40 146

📌 An Open Data-Driven Approach to Optimising Healthcare Facility Locations Using Python 🗂 Category: 🕒 Date: 2024-06-11 | ⏱️ Read time: 15 min read A tutorial in Python with an open data stack

40 146

📌 MLOps – Data Validation with PyTest 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-11 | ⏱️ Read time: 12 min read Run deterministic and non-deterministic tests to validate your dataset

40 146

📌 ASA’s Caution: Rethinking How We Use p-Values in Research 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-11 | ⏱️ Read time: 9 min read Understanding the ASA’s statement to enhance your data science practices

40 146

📌 Deep Learning Illustrated, Part 4: Recurrent Neural Networks 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-11 | ⏱️ Read time: 17 min read An illustrated and intuitive guide on the inner workings of an RNN and the Softmax…

40 146

📌 Spatial Index: Grid Systems 🗂 Category: DATABASE DESIGN 🕒 Date: 2024-06-12 | ⏱️ Read time: 12 min read Grid Systems in Spatial Indexing using GeoHash and Google S2

40 146

📌 The Math Behind KAN – Kolmogorov-Arnold Networks 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-12 | ⏱️ Read time: 15 min read A new alternative to the classic Multi-Layer Perceptron is out. Why is it more accurate…

40 146

📌 How to Pivot Tables in SQL 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-12 | ⏱️ Read time: 12 min read A comprehensive guide to creating pivot tables in SQL for enhanced data analysis

40 146

📌 Model Interpretability Using Credit Card Fraud Data 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-12 | ⏱️ Read time: 20 min read Why model interpretability is important

40 146

📌 Simplifying the Python Code for Data Engineering Projects 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-06-12 | ⏱️ Read time: 12 min read Python tricks and techniques for data ingestion, validation, processing, and testing: a practical walkthrough

40 146

📌 How to Evaluate Retrieval Quality in RAG Pipelines: Precision@k, Recall@k, and F1@k 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-10-16 | ⏱️ Read time: 18 min read In my previous posts, I have walked you through putting together a very basic RAG…

40 146

📌 A Beginner’s Guide to Robotics with Python 🗂 Category: ROBOTICS 🕒 Date: 2025-10-16 | ⏱️ Read time: 9 min read Build 3D simulations with PyBullet

40 146

📌 Stop Feeling Lost : How to Master ML System Design 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-10-16 | ⏱️ Read time: 6 min read What machine learning system design is and how to prepare for it

40 146

📌 Feature Detection, Part 1: Image Derivatives, Gradients, and Sobel Operator 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-10-16 | ⏱️ Read time: 11 min read Applying calculus fundamentals to computer vision for edge detection