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 Siria228 Tecnologías y Aplicaciones3 344...

📈 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 40 221 suscriptores, ocupando la posición 3 344 en la categoría Tecnologías y Aplicaciones y el puesto 228 en la región Siria.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 221 suscriptores.

Según los últimos datos del 03 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 338, y en las últimas 24 horas de 9, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.04%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.42% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 822 visualizaciones. En el primer día suele acumular 973 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 04 julio, 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.

40 221

Suscriptores

+924 horas

+727 días

+33830 días

822

Visitas de la publicación

~ 97324 horas

~ 1 32148 horas

2.04%

Tasa de compromiso

~ 3

Mensajes por día

Ads index

beta

Archivo de publicaciones

40 221

📌 Automatic Differentiation (AutoDiff): A Brief Intro with Examples 🗂 Category: DEEP LEARNING 🕒 Date: 2024-10-11 | ⏱️ Read time: 11 min read An introduction to the mechanics of AutoDiff, exploring its mathematical principles, implementation strategies, and applications

40 221

📌 Topic Alignment for NLP Recommender Systems 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-11 | ⏱️ Read time: 18 min read Leveraging topic modeling to align user queries with document themes, enhancing the relevance and contextual…

40 221

📌 A Mixed-Methods Approach to Offline Evaluation of News Recommender Systems 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-11 | ⏱️ Read time: 8 min read Combining reader feedback from surveys with behavioral click data to optimize content personalization.

40 221

📌 Understanding Automatic Differentiation in JAX: A Deep Dive 🗂 Category: DEEP LEARNING 🕒 Date: 2024-10-11 | ⏱️ Read time: 12 min read Unleashing the Gradient: How JAX Makes Automatic Differentiation Feel Like Magic

40 221

📌 Common Misconceptions About Data Science 🗂 Category: CAREER ADVICE 🕒 Date: 2024-10-11 | ⏱️ Read time: 7 min read Data science advice that you should question

40 221

📌 Bursting the AI Hype Bubble Once and for All 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-12 | ⏱️ Read time: 11 min read Misinformation and poor research: a case study

40 221

📌 Gaussian Naive Bayes, Explained: A Visual Guide with Code Examples for Beginners 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-12 | ⏱️ Read time: 8 min read Bell-shaped assumptions for better predictions

40 221

📌 Improve Your RAG Context Recall by 95% with an Adapted Embedding Model. 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-10-12 | ⏱️ Read time: 11 min read Step by Step Model Adaptation Code and Results Attached.

40 221

📌 Why the 2024 Nobel Prize in (AI for) Chemistry Matters So Much 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-12 | ⏱️ Read time: 6 min read To Demis Hassabis and John Jumper, from DeepMind, and to David Baker, leader of the…

40 221

📌 Upgrading to Prefect Push Workers on AWS ECS 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-10-12 | ⏱️ Read time: 6 min read Upgrade from Prefect 2.0 to 3.0 and use the new Push Work Pools that greatly…

40 221

📌 Linear Discriminant Analysis (LDA) 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-10-12 | ⏱️ Read time: 13 min read Discover how LDA helps identify critical data features

40 221

📌 Top 5 Principles for Building User-Friendly Data Tables 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-10-13 | ⏱️ Read time: 9 min read Designing intuitive and reliable tables that your data team will love

40 221

📌 Recruiting vs. Interviewing for Data Roles in Diverse Markets 🗂 Category: CAREER ADVICE 🕒 Date: 2024-10-13 | ⏱️ Read time: 12 min read Factors of success in recruiting and interviewing after applying for 150+ positions and reviewing 500+…

40 221

📌 How to Perform A/B Testing with Hypothesis Testing in Python: A Comprehensive Guide 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-13 | ⏱️ Read time: 11 min read A Step-by-Step Guide to Making Data-Driven Decisions with Practical Python Examples

40 221

📌 Bringing Structure to Your Data 🗂 Category: 🕒 Date: 2024-10-14 | ⏱️ Read time: 13 min read Testing assumptions with path models

40 221

📌 lintsampler: a new way to quickly get random samples from any distribution 🗂 Category: PROBABILITY 🕒 Date: 2024-10-14 | ⏱️ Read time: 5 min read lintsampler is a pure Python package that can easily and efficiently generate random samples from…

40 221

📌 Product-Oriented ML: A Guide for Data Scientists 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-10-14 | ⏱️ Read time: 30 min read How to build ML products users love

40 221

📌 How to Set Bid Guardrails in PPC Marketing 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-14 | ⏱️ Read time: 14 min read Without controls, bidding algorithms can be quite volatile. Learn how to protect performance through adding…

40 221

📌 PyTorch Optimizers Aren’t Fast Enough. Try These Instead 🗂 Category: DATA SCIENCE 🕒 Date: 2024-10-14 | ⏱️ Read time: 12 min read These 4 advanced optimizers will open your mind.

40 221

📌 Florence-2: Advancing Multiple Vision Tasks with a Single VLM Model 🗂 Category: 🕒 Date: 2024-10-14 | ⏱️ Read time: 8 min read A Guided Exploration of Florence-2’s Zero-Shot Capabilities: Captioning, Object Detection, Segmentation and OCR.