Machine Learning

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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 Syria225 Technologies & Applications3 327...

📈 Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 373 subscribers, ranking 3 327 in the Technologies & Applications category and 225 in the Syria region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 373 subscribers.

According to the latest data from 12 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 399 over the last 30 days and by 24 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 2.42%. Within the first 24 hours after publication, content typically collects 1.74% reactions from the total number of subscribers.
Post reach: On average, each post receives 979 views. Within the first day, a publication typically gains 703 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 4.
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 13 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.

40 373

Subscribers

+2424 hours

+1257 days

+39930 days

979

Post views

~ 70324 hours

~ 95648 hours

2.42%

Engagement rate

~ 2

Posts per day

Ads index

beta

Posts Archive

40 393

📌 Training AI Models on CPU 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-01 | ⏱️ Read time: 16 min read Revisiting CPU for ML in an Era of GPU Scarcity

40 393

📌 Create Your Own Meal Planner Using ChatGPT 🗂 Category: CHATGPT 🕒 Date: 2024-09-02 | ⏱️ Read time: 19 min read A brief guide to prompt engineering

40 393

📌 Mathematics of Love: Optimizing a Dining-Room Seating Arrangement for Weddings with Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-02 | ⏱️ Read time: 19 min read Solving the Restricted Quadratic Multi-Knapsack Problem (RQMKP) with mathematical programming and Python

40 393

📌 An Easy Way to Remove Tourists from Photos 🗂 Category: PYTHON 🕒 Date: 2024-09-02 | ⏱️ Read time: 9 min read Image cleanup with Python, PIL, and OpenCV

40 393

📌 Encoding Categorical Data, Explained: A Visual Guide with Code Example for Beginners 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-02 | ⏱️ Read time: 10 min read Six ways of matchmaking categories and numbers

40 393

📌 Use R to build Clinical Flowchart with shinyCyJS 🗂 Category: 🕒 Date: 2024-09-03 | ⏱️ Read time: 6 min read Customizable R package for Graph / Network visualization

40 393

📌 Subway Route Data Extraction with Overpass API: A Step-by-Step Guide 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-03 | ⏱️ Read time: 11 min read Simplify Geodata Extraction from OpenStreetMaps via the Overpass API

40 393

📌 Information in Noise 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-03 | ⏱️ Read time: 4 min read Two Techniques for Visualizing Many Time-Series at Once

40 393

📌 5 Pillars for a Hyper-Optimized AI Workflow 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-03 | ⏱️ Read time: 8 min read A gentle introduction to a methodology for creating production-ready, extensible & highly optimized AI workflows

40 393

📌 Line-By-Line, Let’s Reproduce GPT-2: Section 3 – Training 🗂 Category: 🕒 Date: 2024-09-03 | ⏱️ Read time: 20 min read This blog post will go line-by-line through the code in Section 3 of Andrej Karpathy’s…

40 393

📌 Using Generative AI To Get Insights From Disorderly Data 🗂 Category: 🕒 Date: 2024-09-03 | ⏱️ Read time: 41 min read Best practices for using Large Language Models to extract actionable insights even with poor metadata

40 393

📌 Here Comes Mamba: The Selective State Space Model 🗂 Category: DEEP LEARNING 🕒 Date: 2024-09-03 | ⏱️ Read time: 22 min read Part 3 – Towards Mamba State Space Models for Images, Videos and Time Series

40 393

📌 Diving Deeper with Structured Outputs 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-09-03 | ⏱️ Read time: 10 min read Enhancing our understanding and optimal usage of structured outputs

40 393

📌 Approximating Stochastic Functions with Multivariate Outputs 🗂 Category: 🕒 Date: 2024-09-04 | ⏱️ Read time: 25 min read A generic approach for training probabilistic machine learning models

40 393

📌 How I Used Clustering to Improve Chunking and Build Better RAGs 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-04 | ⏱️ Read time: 8 min read It’s both fast and cost-effective

40 393

📌 Batch And Streaming Demystified For Unification 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-09-04 | ⏱️ Read time: 29 min read Understand how batch can be considered a subset of streaming and why data engineering should…

40 393

📌 How to Train a Vision Transformer (ViT) from Scratch 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-04 | ⏱️ Read time: 13 min read A practical guide to implementing the Vision Transformer (ViT)

40 393

📌 Hands-On Global Optimization Methods, with Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-04 | ⏱️ Read time: 15 min read Four methods to find the maximum (or minimum) of your black box objective function

40 393

📌 Monte Carlo Methods for Solving Reinforcement Learning Problems 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-04 | ⏱️ Read time: 20 min read Dissecting “Reinforcement Learning” by Richard S. Sutton with Custom Python Implementations, Episode III

40 393

📌 Automated Prompt Engineering: The Definitive Hands-On Guide 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-09-04 | ⏱️ Read time: 26 min read Learn how to automate prompt engineering and unlock significant performance improvements in your LLM workload