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

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📌 Hallucinations in LLMs Are Not a Bug in the Data 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-03-16 | ⏱️ Read time: 10 min read It’s a feature of the architecture #DataScience #AI #Python

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📌 Bayesian Thinking for People Who Hated Statistics 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-16 | ⏱️ Read time: 12 min read You already think like a Bayesian. Your stats class just taught the formula before the… #DataScience #AI #Python

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📌 The Causal Inference Playbook: Advanced Methods Every Data Scientist Should Master 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-15 | ⏱️ Read time: 17 min read Master six advanced causal inference methods with Python: doubly robust estimation, instrumental variables, regression discontinuity,… #DataScience #AI #Python

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📌 The 2026 Data Mandate: Is Your Governance Architecture a Fortress or a Liability? 🗂 Category: DATA GOVERNANCE 🕒 Date: 2026-03-15 | ⏱️ Read time: 8 min read Is your data strategy 2026-ready? Get a deep dive into the mandatory shift toward human-in-the-loop… #DataScience #AI #Python

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📌 The Current Status of The Quantum Software Stack 🗂 Category: QUANTUM COMPUTING 🕒 Date: 2026-03-14 | ⏱️ Read time: 8 min read How do we program quantum computers today? #DataScience #AI #Python

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📌 The Multi-Agent Trap 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-14 | ⏱️ Read time: 12 min read Google DeepMind found multi-agent networks amplify errors 17x. Learn 3 architecture patterns that separate $60M… #DataScience #AI #Python

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📌 Personalized Restaurant Ranking with a Two-Tower Embedding Variant 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-03-13 | ⏱️ Read time: 6 min read How a lightweight two-tower model improved restaurant discovery when popularity ranking failed #DataScience #AI #Python

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📌 How Vision Language Models Are Trained from “Scratch” 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-03-13 | ⏱️ Read time: 13 min read A deep dive into exactly how text-only language models are finetuned to see images #DataScience #AI #Python

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📌 Why Care About Prompt Caching in LLMs? 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-03-13 | ⏱️ Read time: 11 min read Optimizing the cost and latency of your LLM calls with Prompt Caching #DataScience #AI #Python

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Repost from Machine Learning with Python

🗂 Building our own mini-Skynet — a collection of 10 powerful AI repositories from big tech companies 1. Generative AI for Beginners and AI Agents for Beginners Microsoft provides a detailed explanation of generative AI and agent architecture: from theory to practice. 2. LLMs from Scratch Step-by-step assembly of your own GPT to understand how LLMs are structured "under the hood". 3. OpenAI Cookbook An official set of examples for working with APIs, RAG systems, and integrating AI into production from OpenAI. 4. Segment Anything and Stable Diffusion Classic tools for computer vision and image generation from Meta and the CompVis research team. 5. Python 100 Days and Python Data Science Handbook A powerful resource for Python and data analysis. 6. LLM App Templates and ML for Beginners Ready-made app templates with LLMs and a structured course on classic machine learning. If you want to delve deeply into AI or start building your own projects — this is an excellent starting kit. tags: #github #LLM #AI #ML ➡️ https://t.me/CodeProgrammer

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📌 How to Build Agentic RAG with Hybrid Search 🗂 Category: RAG 🕒 Date: 2026-03-13 | ⏱️ Read time: 7 min read Learn how to build a powerful agentic RAG system #DataScience #AI #Python

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📌 A Tale of Two Variances: Why NumPy and Pandas Give Different Answers 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-13 | ⏱️ Read time: 7 min read Imagine you are analyzing a small dataset: You want to calculate some summary statistics to… #DataScience #AI #Python

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📌 I Finally Built My First AI App (And It Wasn’t What I Expected) 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-03-12 | ⏱️ Read time: 14 min read A beginner-friendly walkthrough of API calls, environment variables, and real-world AI infrastructure #DataScience #AI #Python

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📌 Scaling Vector Search: Comparing Quantization and Matryoshka Embeddings for 80% Cost Reduction 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-03-12 | ⏱️ Read time: 11 min read Navigating the performance cliff: How pairing MRL with int8 and binary quantization balances infrastructure costs… #DataScience #AI #Python

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📌 Solving the Human Training Data Problem 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-03-12 | ⏱️ Read time: 18 min read How AI has completely transformed the way I study as a graduate student #DataScience #AI #Python

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Repost from Machine Learning with Python

Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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Repost from Machine Learning with Python

Machine Learning in Python (Course Notes) I just went through an amazing resource on #MachineLearning in #Python by 365 Data Science, and I had to share the key takeaways with you! Here’s what you’ll learn: 🔘 Linear Regression - The foundation of predictive modeling 🔘 Logistic Regression - Predicting probabilities and classifications 🔘 Clustering (K-Means, Hierarchical) - Making sense of unstructured data 🔘 Overfitting vs. Underfitting - The balancing act every ML engineer must master 🔘 OLS, R-squared, F-test - Key metrics to evaluate your models https://t.me/CodeProgrammer || Share 🌐 and Like 👍

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📌 Exploratory Data Analysis for Credit Scoring with Python 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-12 | ⏱️ Read time: 16 min read Understanding default risk through statistical analysis of borrower and loan characteristics. #DataScience #AI #Python