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 Syria228 Technologies & Applications3 352...

📈 Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 205 subscribers, ranking 3 352 in the Technologies & Applications category and 228 in the Syria region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 205 subscribers.

According to the latest data from 02 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 343 over the last 30 days and by 10 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 1.99%. Within the first 24 hours after publication, content typically collects 2.28% reactions from the total number of subscribers.
Post reach: On average, each post receives 800 views. Within the first day, a publication typically gains 915 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.

📝 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 03 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.

40 205

Subscribers

+1024 hours

+837 days

+34330 days

800

Post views

~ 91524 hours

~ 1 23548 hours

1.99%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

40 207

📌 Data Visualization Explained (Part 2): An Introduction to Visual Variables 🗂 Category: DATA VISUALIZATION 🕒 Date: 2025-10-01 | ⏱️ Read time: 7 min read A non-technical and accessible guide to the underlying concept behind visual design: visual encoding channels

40 207

📌 How to Improve the Efficiency of Your PyTorch Training Loop 🗂 Category: DEEP LEARNING 🕒 Date: 2025-10-01 | ⏱️ Read time: 14 min read Learn how to diagnose and resolve bottlenecks in PyTorch using the numworkers, pinmemory, and profiler…

40 207

📌 Are Foundation Models Ready for Your Production Tabular Data? 🗂 Category: LARGE DATA MODELS 🕒 Date: 2025-10-01 | ⏱️ Read time: 15 min read A complete review of architectures to make zero-shot predictions in the most common types of…

40 207

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40 207

“I turned $1,000 into $4,500 in just 2 weeks — but nobody believed me until they saw my account.” Want to know the exact signals I used? The secret’s hidden right here — but hurry, only a few will see this in time. #ad InsideAds

40 207

📌 The Data Strategy Choice Cascade 🗂 Category: 🕒 Date: 2024-09-16 | ⏱️ Read time: 23 min read What your data strategy should look like

40 207

📌 How to Implement State-of-the-Art Masked AutoEncoders (MAE) 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 8 min read A Step-by-Step Guide to Building MAE with Vision Transformers

40 207

📌 Unit Disk Uniform Sampling 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 15 min read Discover the optimal transformations to apply on the standard 0,1 uniform random generator for uniformly…

40 207

📌 Vision Mamba: Like a Vision Transformer but Better 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 26 min read Part 4 – Towards Mamba State Space Models for Images, Videos and Time Series

40 207

📌 Teaching Your Model to Learn from Itself 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 6 min read In machine learning, more data leads to better results. But labeling data can be expensive…

40 207

📌 Disability, Accessibility, and AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 11 min read A discussion of how AI can help and harm people with disabilities

40 207

📌 Introducing NumPy, Part 4: Doing Math with Arrays 🗂 Category: 🕒 Date: 2024-09-16 | ⏱️ Read time: 12 min read Plus reading and writing array data!

40 207

📌 PySpark Explained: The InferSchema Problem 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 10 min read Think before using this common option when reading large CSV’s

40 207

“Nobody believed you could grow small capital—until I saw this.” $1,000 turned into real profit before my eyes. The secret? Bonus fuel & copytrading with Elite Gold. Want proof? See how it’s actually done before the bonus ends. #ad InsideAds

40 207

📌 Football and Geometry – Passing Networks 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 12 min read Analyzing Bayer Leverkusen’s Passing Networks from Last Season

40 207

📌 Model Management with MLflow, Azure, and Docker 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-17 | ⏱️ Read time: 11 min read A guide to tracking experiments and managing models

40 207

📌 The Math Behind Kernel Density Estimation 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-17 | ⏱️ Read time: 13 min read Exploring the foundations, concepts, and math of kernel density estimation

40 207

📌 Polars + NVIDIA GPU Tutorial 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-17 | ⏱️ Read time: 4 min read Using Polars with NVIDIA GPU can speed up your data pipelines

40 207

📌 GPU Accelerated Polars – Intuitively and Exhaustively Explained 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-17 | ⏱️ Read time: 16 min read Fast Dataframes for Big Problems

40 207

Repost from Machine Learning with Python

Today I am 3️⃣0️⃣ years old, I am excited to make more successes and achievements My previous year was full of exciting events and economic, political and programmatic noise, but I kept moving forward Best regards Eng. @HusseinSheikho 🔤