Available now! Telegram Research 2025 β the year's key insights 
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
Show moreπ 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.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄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.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βReal Machine Learning β simple, practical, and built on experience.
Learn step by step with clear explanations and working code.
Admin: @HusseinSheikho || @Hussein_Sheikhoβ
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
40 209
π Nine Rules for Running Rust on WASM WASI
π Category: PROGRAMMING
π Date: 2024-09-28 | β±οΈ Read time: 16 min read
Practical Lessons from Porting range-set-blaze to this Container-Like Environment
40 209
π Model Deployment with FastAPI, Azure, and Docker
π Category: MACHINE LEARNING
π Date: 2024-09-28 | β±οΈ Read time: 11 min read
A Complete Guide to Serving a Machine Learning Model with FastAPI
40 209
π Exploring the Link between Sleep Disorders and Health Indicators
π Category: DATA SCIENCE
π Date: 2024-09-28 | β±οΈ Read time: 16 min read
A Python analysis of a MIMIC-IV health data (DREAMT) to uncover insights into factors affectingβ¦
40 209
π Hands-On Optimization Using Genetic Algorithms, with Python
π Category: MACHINE LEARNING
π Date: 2024-09-29 | β±οΈ Read time: 15 min read
Hereβs a full guide on genetic algorithms, what they are, and how to use them
40 209
π How to Get Pull Request Data Using GitHub API
π Category: DATA SCIENCE
π Date: 2024-09-29 | β±οΈ Read time: 5 min read
Getting the diff between any two commits
40 209
π Whatβs Inside a Neural Network?
π Category: DATA SCIENCE
π Date: 2024-09-29 | β±οΈ Read time: 5 min read
Plotting surface of error in 3D using PyTorch
40 209
π To Mask or Not to Mask: The Effect of Prompt Tokens on Instruction Tuning
π Category: ARTIFICIAL INTELLIGENCE
π Date: 2024-09-30 | β±οΈ Read time: 37 min read
Implementing prompt-loss-weight, and why we should replace prompt-masking with prompt-weighting
40 209
π Eulerian Melodies: Graph Algorithms for Music Composition
π Category: GRAPH THEORY
π Date: 2025-09-28 | β±οΈ Read time: 15 min read
Conceptual overview and an end-to-end Python implementation
40 209
Repost from Machine Learning with Python
π³οΈβπ Learning Python for science is
β
with these 8 awesome GitHub repos!
π₯ Repo: Project Based Learning
π¬ One of the most famous educational repos with 230K+ stars that implements various algorithms and projects using Python.
β β β
π₯ Repo: Real Python Materials
π¬ Supplementary resources and exercises including project-based tutorials, guides, and practical exercises.
β β β
π₯ Repo: Learn By Doing
π¬ Project-based tutorials in AI and machine learning for all levels.
β β β
π₯ Repo: Awesome Jupyter
π¬ A curated collection of notebooks, tools, and powerful libraries for working with Jupyter.
β β β
π₯ Repo: Python Mini Projects
π¬ A collection of mini-projects like games and small apps that you can quickly run and practice.
β β β
π₯ Repo: 100Projects of Code
π¬ An educational challenge including 100 real projects; you practice and see your progress day by day.
β β β
π₯ Repo: Data Science Projects
π¬ Practical ideas and examples to start data science with Python.
β β β
π₯ Repo: Python Project Scripts
π¬ Small and large scripting projects, from beginner to advanced levels.
By: https://t.me/CodeProgrammer βοΈ
40 209
π The AI Developerβs Dilemma: Proprietary AI vs. Open Source Ecosystem
π Category: ARTIFICIAL INTELLIGENCE
π Date: 2024-09-30 | β±οΈ Read time: 20 min read
Fundamental Choices Impacting Integration and Deployment at Scale of GenAI into Businesses
40 209
π Evaluating Train-Test Split Strategies in Machine Learning: Beyond the Basics
π Category: DATA SCIENCE
π Date: 2024-09-30 | β±οΈ Read time: 6 min read
Creating Appropriate Test Sets and Sleeping Soundly.
40 209
π Steinβs Paradox
π Category: DATA SCIENCE
π Date: 2024-09-30 | β±οΈ Read time: 8 min read
Why the Sample Mean Isnβt Always the Best
40 209
π Is Less More? Do Deep Learning Forecasting Models Need Feature Reduction?
π Category: ANALYTICS
π Date: 2024-09-30 | β±οΈ Read time: 14 min read
To curate, or not to curate, that is the question
40 209
π Exploring the World of Markov Chains: Unlocking the Power of Probabilistic Transitions
π Category: PROBABILITY
π Date: 2024-09-30 | β±οΈ Read time: 11 min read
An Introduction to Markov Chains, their applications, and how to use Monte Carlo Simulations inβ¦
40 209
π 5 Must-Know Techniques for Mastering Time-Series Analysis
π Category: DATA SCIENCE
π Date: 2024-09-30 | β±οΈ Read time: 22 min read
Elevate Your Machine Learning Forecasting with Accurate Data Splitting, Time-Series Cross-Validation, Feature Engineering, and More!
40 209
π Evaluating performance of LLM-based Applications
π Category:
π Date: 2024-09-30 | β±οΈ Read time: 9 min read
Evaluation Framework for real-world requirements
40 209
π Can Transformers Solve Everything?
π Category: MACHINE LEARNING
π Date: 2024-10-01 | β±οΈ Read time: 15 min read
Looking into the math and the data reveals that transformers are both overused and underused.
40 209
π Support Vector Classifier, Explained: A Visual Guide with Mini 2D Dataset
π Category: DATA SCIENCE
π Date: 2024-10-01 | β±οΈ Read time: 17 min read
Finding the best βlineβ to separate the classes? Yeah, sureβ¦
40 209
π What I Learned in my First 9 Months as a Freelance Data Scientist
π Category: DATA SCIENCE
π Date: 2024-10-01 | β±οΈ Read time: 24 min read
Observations and lessons learned from in the trenches
40 209
π Graph Neural Networks Part 1. Graph Convolutional Networks Explained
π Category:
π Date: 2024-10-01 | β±οΈ Read time: 12 min read
Node classification with Graph Convolutional Networks
