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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
40 068
📌 How to Become an AI Engineer Fast (Skills, Projects, Salary)
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-03-29 | ⏱️ Read time: 12 min read
Spoiler, it will take longer than 3 months
#DataScience #AI #Python
40 068
📌 Self-Healing Neural Networks in PyTorch: Fix Model Drift in Real Time Without Retraining
🗂 Category: DEEP LEARNING
🕒 Date: 2026-03-29 | ⏱️ Read time: 22 min read
What happens when your production model drifts and retraining isn’t an option? This article shows…
#DataScience #AI #Python
40 068
📌 Using OpenClaw as a Force Multiplier: What One Person Can Ship with Autonomous Agents
🗂 Category: AGENTIC AI
🕒 Date: 2026-03-28 | ⏱️ Read time: 25 min read
It’s easier than ever to 10x your output with agentic AI.
#DataScience #AI #Python
40 068
📌 From NetCDF to Insights: A Practical Pipeline for City-Level Climate Risk Analysis
🗂 Category: CLIMATE CHANGE
🕒 Date: 2026-03-28 | ⏱️ Read time: 7 min read
Integrating CMIP6 projections, ERA5 reanalysis, and impact models into a lightweight, interpretable workflow
#DataScience #AI #Python
40 068
📌 How ElevenLabs Voice AI Is Replacing Screens in Warehouse and Manufacturing Operations
🗂 Category: DATA SCIENCE
🕒 Date: 2026-03-27 | ⏱️ Read time: 10 min read
A warehouse picking operation is the process of collecting items from storage locations to fulfil…
#DataScience #AI #Python
40 068
📌 A Beginner’s Guide to Quantum Computing with Python
🗂 Category: QUANTUM COMPUTING
🕒 Date: 2026-03-27 | ⏱️ Read time: 7 min read
Simulate a quantum computer with Qiskit
#DataScience #AI #Python
40 068
📌 Building a Production-Grade Multi-Node Training Pipeline with PyTorch DDP
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-03-27 | ⏱️ Read time: 14 min read
A practical, code-driven guide to scaling deep learning across machines — from NCCL process groups…
#DataScience #AI #Python
40 068
Repost from Machine Learning with Python
Classical filters & convolution: The heart of computer vision
Before Deep Learning exploded onto the scene, traditional computer vision centered on filters. Filters were small, hand-engineered matrices that you convolved with an image to detect specific features like edges, corners, or textures. In this article, we will dive into the details of classical filters and convolution operation - how they work, why they matter, and how to implement them.
More: https://www.vizuaranewsletter.com/p/classical-filters-and-convolution
40 068
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40 068
📌 What the Bits-over-Random Metric Changed in How I Think About RAG and Agents
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-03-26 | ⏱️ Read time: 19 min read
Why retrieval that looks excellent on paper can still behave like noise in real RAG…
#DataScience #AI #Python
40 068
📌 Beyond Code Generation: AI for the Full Data Science Workflow
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-03-26 | ⏱️ Read time: 10 min read
Using Codex and MCP to connect Google Drive, GitHub, BigQuery, and analysis in one real workflow
#DataScience #AI #Python
40 068
📌 How to Make Your AI App Faster and More Interactive with Response Streaming
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-03-26 | ⏱️ Read time: 8 min read
In my latest posts, we’ve talked a lot about prompt caching as well as caching…
#DataScience #AI #Python
40 068
📌 My Models Failed. That’s How I Became a Better Data Scientist.
🗂 Category: DATA SCIENCE
🕒 Date: 2026-03-25 | ⏱️ Read time: 9 min read
Data Leakage, Real-World Models, and the Path to Production AI in Healthcare
#DataScience #AI #Python
40 068
📌 Building Human-In-The-Loop Agentic Workflows
🗂 Category: AGENTIC AI
🕒 Date: 2026-03-25 | ⏱️ Read time: 10 min read
Understanding how to set up human-in-the-loop (HITL) agentic workflows in LangGraph
#DataScience #AI #Python
40 068
📌 The Machine Learning Lessons I’ve Learned This Month
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-03-25 | ⏱️ Read time: 5 min read
Proactivity, blocking, and planning
#DataScience #AI #Python
40 068
📌 Following Up on Like-for-Like for Stores: Handling PY
🗂 Category: DATA ANALYSIS
🕒 Date: 2026-03-25 | ⏱️ Read time: 7 min read
My last article was about implementing Like-for-Like (L4L) for Stores. After discussing my solution with…
#DataScience #AI #Python
40 068
📌 The Complete Guide to AI Implementation for Chief Data & AI Officers in 2026
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-03-24 | ⏱️ Read time: 29 min read
How to leverage a framework to effectively prioritize AI Initiatives to rapidly accelerate growth and…
#DataScience #AI #Python
40 068
📌 Production-Ready LLM Agents: A Comprehensive Framework for Offline Evaluation
🗂 Category: AGENTIC AI
🕒 Date: 2026-03-24 | ⏱️ Read time: 18 min read
We’ve become remarkably good at building sophisticated agent systems, but we haven’t developed the same…
#DataScience #AI #Python
40 068
📌 From Dashboards to Decisions: Rethinking Data & Analytics in the Age of AI
🗂 Category: DATA SCIENCE
🕒 Date: 2026-03-24 | ⏱️ Read time: 7 min read
How AI agents, data foundations, and human-centered analytics are reshaping the future of decision-making
#DataScience #AI #Python
40 068
Repost from Machine Learning with Python
Visualizing the complexity of algorithms
https://t.me/CodeProgrammer ✅
