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 40 072 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

770

Visitas de la publicación

~ 46624 horas

~ 62048 horas

1.92%

Tasa de compromiso

~ 3

Mensajes por día

Ads index

beta

Archivo de publicaciones

40 077

📌 Why Most A/B Tests Are Lying to You 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-11 | ⏱️ Read time: 14 min read The 4 statistical sins that invalidate most A/B tests, plus a pre-test checklist and Bayesian… #DataScience #AI #Python

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📌 Spectral Clustering Explained: How Eigenvectors Reveal Complex Cluster Structures 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-03-11 | ⏱️ Read time: 10 min read Understanding why spectral clustering outperforms K-means #DataScience #AI #Python

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📌 An Intuitive Guide to MCMC (Part I): The Metropolis-Hastings Algorithm 🗂 Category: MATH 🕒 Date: 2026-03-11 | ⏱️ Read time: 14 min read Tired of the AI hype? Let’s talk about the probabilistic algorithms actually driving high-end quantitative… #DataScience #AI #Python

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📌 How the Fourier Transform Converts Sound Into Frequencies 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-03-11 | ⏱️ Read time: 26 min read A visual, intuition-first guide to understanding what the math is really doing — from winding… #DataScience #AI #Python

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Repost from Machine Learning with Python

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📌 When Data Lies: Finding Optimal Strategies for Penalty Kicks with Game Theory 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-10 | ⏱️ Read time: 9 min read A data-driven introduction to game theory, Nash equilibrium, and strategic decision-making #DataScience #AI #Python

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📌 Hybrid Neuro-Symbolic Fraud Detection: Guiding Neural Networks with Domain Rules 🗂 Category: DEEP LEARNING 🕒 Date: 2026-03-10 | ⏱️ Read time: 14 min read I really thought I was onto something big: add a couple of simple domain rules… #DataScience #AI #Python

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Repost from Machine Learning with Python

🗂 A fresh deep learning course from MIT is now publicly available A full-fledged educational course has been published on the university's website: 24 lectures, practical assignments, homework, and a collection of materials for self-study. The program includes modern neural network architectures, generative models, transformers, inference, and other key topics. ➡️ Link to the course tags: #Python #DataScience #DeepLearning #AI

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📌 What Are Agent Skills Beyond Claude? 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-10 | ⏱️ Read time: 6 min read How to design and implement agent skills for custom agents outside the Claude ecosystem #DataScience #AI #Python

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📌 Building a Like-for-Like solution for Stores in Power BI 🗂 Category: DATA ANALYSIS 🕒 Date: 2026-03-10 | ⏱️ Read time: 10 min read Like-for-Like (L4L) solutions are essential for comparing elements. It’s about comparing only comparable elements, in… #DataScience #AI #Python

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📌 I Stole a Wall Street Trick to Solve a Google Trends Data Problem 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-09 | ⏱️ Read time: 14 min read A methodology for comparing Google Trends data across countries. #DataScience #AI #Python

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Repost from Learn Python Coding

This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

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Repost from Machine Learning with Python

🧠 Python libraries for AI agents - complexity of learning 🔥 🟢 Easy • LangChain • tool calling • agent memory • simple agents • CrewAI • agents with roles • collaboration of several agents • SmolAgents • lightweight agents • quick experiments 🟡 Medium • LangGraph • stateful workflow • agent orchestration • LlamaIndex • RAG pipelines • data indexing • knowledge agents • OpenAI Agents SDK • tool integrations • agent workflows • Strands • agent orchestration • task coordination • Semantic Kernel • skills / plugins • AI process orchestration • PydanticAI • typed LLM applications • structured agent workflows • Langroid • message exchange between agents • interaction with tools 🔴 Difficult • AutoGen • multi-agent dialogues • autonomous agent cooperation • DSPy • programmable prompting • optimization of LLM pipelines • A2A • agent-to-agent protocol • distributed agent systems https://t.me/CodeProgrammer ✅

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📌 Three OpenClaw Mistakes to Avoid and How to Fix Them 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-09 | ⏱️ Read time: 7 min read Learn how to set up OpenClaw effectively #DataScience #AI #Python

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📌 Machine Learning at Scale: Managing More Than One Model in Production 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-03-09 | ⏱️ Read time: 7 min read From one model to managing a massive portfolio: What 10 years in the industry taught… #DataScience #AI #Python

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📌 Write C Code Without Learning C: The Magic of PythoC 🗂 Category: PROGRAMMING 🕒 Date: 2026-03-08 | ⏱️ Read time: 9 min read Compile native, standalone applications using the Python syntax you already know. #DataScience #AI #Python

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📌 LatentVLA: Latent Reasoning Models for Autonomous Driving 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-08 | ⏱️ Read time: 8 min read What if natural language is not the best abstraction for driving? #DataScience #AI #Python

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📌 The AI Bubble Has a Data Science Escape Hatch 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-07 | ⏱️ Read time: 12 min read Five classical data science skills are becoming the scarcest resource in tech. A 90-day roadmap… #DataScience #AI #Python

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📌 Understanding Context and Contextual Retrieval in RAG 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-03-07 | ⏱️ Read time: 10 min read Why traditional RAG loses context and how contextual retrieval dramatically improves retrieval accuracy #DataScience #AI #Python

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📌 What Makes Quantum Machine Learning “Quantum”? 🗂 Category: QUANTUM COMPUTING 🕒 Date: 2026-03-06 | ⏱️ Read time: 8 min read And where is it today? #DataScience #AI #Python