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 Syria231 Technologies & Applications3 384...

📈 Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 106 subscribers, ranking 3 384 in the Technologies & Applications category and 231 in the Syria region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 106 subscribers.

According to the latest data from 24 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 401 over the last 30 days and by 38 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 1.16% reactions from the total number of subscribers.
Post reach: On average, each post receives 788 views. Within the first day, a publication typically gains 465 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 25 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.

40 106

Subscribers

+3824 hours

+637 days

+40130 days

788

Post views

~ 46524 hours

~ 62048 hours

1.96%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

40 106

📌 Why Healthcare Leads in Knowledge Graphs 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-18 | ⏱️ Read time: 9 min read How science, regulation, collaboration, and public funding shaped the world’s most mature semantic infrastructure #DataScience #AI #Python

40 106

Adakah anda merasakan analisis anda sentiasa kekurangan rangka kerja？Kami telah menubuhkan forum perbincangan mendalam yang memberi t Adakah anda merasakan analisis anda sentiasa kekurangan rangka kerja？Kami telah menubuhkan forum perbincangan mendalam yang memberi t Sponsored By WaybienAds

40 106

📌 The Hidden Opportunity in AI Workflow Automation with n8n for Low-Tech Companies 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-18 | ⏱️ Read time: 14 min read How to use n8n with multimodal AI and optimisation tools to help companies with low… #DataScience #AI #Python

40 106

Repost from Machine Learning with Python

Best GitHub repositories to learn AI from scratch in 2026:

1. Andrej Karpathy https://github.com/karpathy/nn-zero-to-hero 2. Hugging Face Transformers https://github.com/huggingface/transformers 3. FastAI/fastbook https://github.com/fastai/fastbook 4. Made-With-ML https://github.com/GokuMohandas/Made-With-ML 5. ML System Design https://github.com/chiphuyen/machine-learning-systems-design 6. Awesome Generative AI guide https://github.com/aishwaryanr/awesome-generative-ai-guide 7. Dive into Deep Learning https://github.com/d2l-ai/d2l-en 🪞 @codeprogrammer Like & Share

40 106

📌 A Geometric Method to Spot Hallucinations Without an LLM Judge 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-17 | ⏱️ Read time: 7 min read Imagine a flock of birds in flight. There’s no leader. No central command. Each bird… #DataScience #AI #Python

40 106

📌 Data Poisoning in Machine Learning: Why and How People Manipulate Training Data 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-01-17 | ⏱️ Read time: 14 min read Do you know where your data has been? #DataScience #AI #Python

40 106

Repost from Machine Learning with Python

🤖 Machine Learning Tutorials Repository 1. Python 2. Computer Vision: Techniques, algorithms 3. NLP 4. Matplotlib 5. NumPy 6. Pandas 7. MLOps 8. LLMs 9. PyTorch/TensorFlow git clone https://github.com/patchy631/machine-learning 🔗 GitHub: https://github.com/patchy631/machine-learning/tree/main ⭐️ https://t.me/DataScienceT

40 106

📌 The Great Data Closure: Why Databricks and Snowflake Are Hitting Their Ceiling 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-16 | ⏱️ Read time: 13 min read Acquisitions, venture, and an increasingly competitive landscape all point to a market ceiling #DataScience #AI #Python

40 106

📌 From RGB to Lab: Addressing Color Artifacts in AI Image Compositing 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-16 | ⏱️ Read time: 13 min read A multi-tier approach to segmentation, color correction, and domain-specific enhancement #DataScience #AI #Python

40 106

📌 Cutting LLM Memory by 84%: A Deep Dive into Fused Kernels 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-16 | ⏱️ Read time: 18 min read Why your final LLM layer is OOMing and how to fix it with a custom… #DataScience #AI #Python

40 106

👍 Top Channels on Telegram 🌟

40 106

Repost from Machine Learning with Python

YOLO Training Template Manual data labeling has become significantly more convenient. Now the process looks like in the usual labeling systems - you just outline the object with a frame and a bounding box is immediately created. The platform allows: • to upload your own dataset • to label manually or auto-label via DINOv3 • to enrich the data if desired • to train a #YOLO model on your own data • to run inference immediately • to export to ONNX or NCNN, which ensures compatibility with edge hardware and smartphones All of this is available for free and can already be tested on #GitHub. Repo: https://github.com/computer-vision-with-marco/yolo-training-template https://t.me/CodeProgrammer

40 106

📌 Maximum-Effiency Coding Setup 🗂 Category: PROGRAMMING 🕒 Date: 2026-01-16 | ⏱️ Read time: 9 min read Learn how to be a more efficient programmer #DataScience #AI #Python

40 106

📌 Do You Smell That? Hidden Technical Debt in AI Development 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-15 | ⏱️ Read time: 14 min read Why speed without standards creates fragile AI products #DataScience #AI #Python

40 106

📌 The 2026 Goal Tracker: How I Built a Data-Driven Vision Board Using Python, Streamlit, and Neon 🗂 Category: PRODUCTIVITY 🕒 Date: 2026-01-15 | ⏱️ Read time: 8 min read Designing a centralized system to track daily habits and long-term goals #DataScience #AI #Python

40 106

📌 How to Run Coding Agents in Parallel 🗂 Category: AGENTIC AI 🕒 Date: 2026-01-15 | ⏱️ Read time: 8 min read Get the most out of Claude Code #DataScience #AI #Python

40 106

📌 When Shapley Values Break: A Guide to Robust Model Explainability 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-01-15 | ⏱️ Read time: 9 min read Shapley Values are one of the most common methods for explainability, yet they can be… #DataScience #AI #Python