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

Red:Machine Learning with Python Siria232 Tecnologías y Aplicaciones3 398...

📈 Análisis del canal de Telegram Machine Learning

El canal Machine Learning (@machinelearning9) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 40 100 suscriptores, ocupando la posición 3 398 en la categoría Tecnologías y Aplicaciones y el puesto 232 en la región Siria.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 100 suscriptores.

Según los últimos datos del 23 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 379, y en las últimas 24 horas de 30, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.92%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.16% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 770 visualizaciones. En el primer día suele acumular 466 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
Intereses temáticos: El contenido se centra en temas clave como distance, insidead, gpu, learning, degree.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 24 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.

40 100

Suscriptores

+3024 horas

+337 días

+37930 días

770

Visitas de la publicación

~ 46624 horas

~ 62048 horas

1.92%

Tasa de compromiso

~ 3

Mensajes por día

Ads index

beta

Archivo de publicaciones

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

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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

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📌 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

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👍 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