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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
Mostrar más📈 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 40 072 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 40 072 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.
40 072
Suscriptores
+3024 horas
+337 días
+37930 días
Archivo de publicaciones
40 085
📌 The Machine Learning Lessons I’ve Learned This Month
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-03-02 | ⏱️ Read time: 6 min read
February 2026: exchange with others, documentation, and MLOps
#DataScience #AI #Python
40 085
Repost from Machine Learning with Python
Excellent free courses on neural networks from Nvidia— the company decided to share knowledge that usually costs 90 dollars.
Here's everything important: video processing, app development, robotics, and much more. An electronic certificate is issued upon completion of the training.
We gain useful knowledge —
https://developer.nvidia.com/join-nvidia-developer-program
https://t.me/CodeProgrammer 🌟
40 085
📌 YOLOv3 Paper Walkthrough: Even Better, But Not That Much
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-03-02 | ⏱️ Read time: 24 min read
A PyTorch implementation on the YOLOv3 architecture from scratch
#DataScience #AI #Python
40 085
📌 Exciting Changes Are Coming to the TDS Author Payment Program
🗂 Category: WRITING
🕒 Date: 2026-03-02 | ⏱️ Read time: 2 min read
Authors can now benefit from updated earning tiers and a higher article cap
#DataScience #AI #Python
40 085
📌 Context Engineering as Your Competitive Edge
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-03-01 | ⏱️ Read time: 13 min read
If you have both unique domain expertise and know how to make it usable to…
#DataScience #AI #Python
40 085
📌 Zero-Waste Agentic RAG: Designing Caching Architectures to Minimize Latency and LLM Costs at Scale
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-03-01 | ⏱️ Read time: 19 min read
Reducing LLM costs by 30% with validation-aware, multi-tier caching
#DataScience #AI #Python
40 085
📌 Scaling ML Inference on Databricks: Liquid or Partitioned? Salted or Not?
🗂 Category: DATA ENGINEERING
🕒 Date: 2026-02-28 | ⏱️ Read time: 11 min read
A case study on techniques to maximize your clusters
#DataScience #AI #Python
40 085
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40 085
📌 Claude Skills and Subagents: Escaping the Prompt Engineering Hamster Wheel
🗂 Category: AGENTIC AI
🕒 Date: 2026-02-28 | ⏱️ Read time: 17 min read
How reusable, lazy-loaded instructions solve the context bloat problem in AI-assisted development.
#DataScience #AI #Python
40 085
📌 The Gap Between Junior and Senior Data Scientists Isn’t Code
🗂 Category: DATA SCIENCE
🕒 Date: 2026-02-27 | ⏱️ Read time: 6 min read
Why my obsession with complex algorithms was actually holding my career back.
#DataScience #AI #Python
40 085
📌 Generative AI, Discriminative Human
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-02-27 | ⏱️ Read time: 14 min read
How to think critically about AI in an ocean of hype
#DataScience #AI #Python
40 085
📌 Stop Asking if a Model Is Interpretable
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-02-27 | ⏱️ Read time: 6 min read
Start asking what question the explanation should answer.
#DataScience #AI #Python
40 085
📌 Coding the Pong Game from Scratch in Python
🗂 Category: PROGRAMMING
🕒 Date: 2026-02-27 | ⏱️ Read time: 18 min read
Implementing the classic Pong game in Python using OOP and Turtle
#DataScience #AI #Python
40 085
📌 Take a Deep Dive into Filtering in DAX
🗂 Category: DATA ANALYSIS
🕒 Date: 2026-02-26 | ⏱️ Read time: 13 min read
Have you ever wondered what happens when you apply a filter in a DAX expression?…
#DataScience #AI #Python
40 085
📌 Designing Data and AI Systems That Hold Up in Production
🗂 Category: AUTHOR SPOTLIGHTS
🕒 Date: 2026-02-26 | ⏱️ Read time: 6 min read
A system-level perspective on architecture, agents, and responsible scale
#DataScience #AI #Python
40 085
Repost from Machine Learning with Python
Free access to over 40 courses
https://lve.to/jwxfnss0yi
40 085
📌 Detecting and Editing Visual Objects with Gemini
🗂 Category: LLM APPLICATIONS
🕒 Date: 2026-02-26 | ⏱️ Read time: 34 min read
A practical guide to identifying, restoring, and transforming elements within your images
#DataScience #AI #Python
40 085
📌 A Generalizable MARL-LP Approach for Scheduling in Logistics
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-02-26 | ⏱️ Read time: 17 min read
Part 1. Hybrid Solution for Dynamic Vehicle Routing — Context and Architecture
#DataScience #AI #Python
40 085
📌 Breaking the Host Memory Bottleneck: How Peer Direct Transformed Gaudi’s Cloud Performance
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-02-25 | ⏱️ Read time: 9 min read
Engineering RDMA-like performance over cloud host NICs using libfabric, DMA-BUF, and HCCL to restore distributed…
#DataScience #AI #Python
40 085
📌 Scaling Feature Engineering Pipelines with Feast and Ray
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-02-25 | ⏱️ Read time: 11 min read
Utilizing feature stores like Feast and distributed compute frameworks like Ray in production machine learning systems
#DataScience #AI #Python
