Machine Learning

Ir al canal en Telegram

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 40 100 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 40 100 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.

40 100

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

40 105

📌 Achieving 5x Agentic Coding Performance with Few-Shot Prompting 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-23 | ⏱️ Read time: 9 min read Learn to leverage few-shot prompting to increase your LLMs performance #DataScience #AI #Python

40 105

📌 Optimizing Data Transfer in Distributed AI/ML Training Workloads 🗂 Category: DATA ENGINEERING 🕒 Date: 2026-01-23 | ⏱️ Read time: 15 min read A deep dive on data transfer bottlenecks, their identification, and their resolution with the help… #DataScience #AI #Python

40 105

📌 What Other Industries Can Learn from Healthcare’s Knowledge Graphs 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-22 | ⏱️ Read time: 11 min read How shared meaning, evidence, and standards create durable semantic infrastructure #DataScience #AI #Python

40 105

📌 Stop Writing Messy Boolean Masks: 10 Elegant Ways to Filter Pandas DataFrames 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-22 | ⏱️ Read time: 7 min read Master the art of readable, high-performance data selection using .query(), .isin(), and advanced vectorized logic. #DataScience #AI #Python

40 105

📌 Why SaaS Product Management Is the Best Domain for Data-Driven Professionals in 2026 🗂 Category: PRODUCT MANAGEMENT 🕒 Date: 2026-01-22 | ⏱️ Read time: 14 min read How I use analytics, automation, and AI to build better SaaS #DataScience #AI #Python

40 105

📌 Evaluating Multi-Step LLM-Generated Content: Why Customer Journeys Require Structural Metrics 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-22 | ⏱️ Read time: 13 min read How to evaluate goal-oriented content designed to build engagement and deliver business results, and why… #DataScience #AI #Python

40 105

📌 A Case for the T-statistic 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-21 | ⏱️ Read time: 21 min read And how it compares to the run-of-the-mill z-score #DataScience #AI #Python

40 105

Guide to AI Coding Agents & Assistants: How to Choose the Right One There are now so many AI tools for coding that it can be confusing to know which one to pick. Some act as simple helpers (Assistant), while others can do the work for you (Agent). This guide breaks down the top AI coding tools that you should be aware of. We will look at what they do, who they are for, and how much they cost. Read: https://habr.com/en/articles/979402/ https://t.me/DataScienceM

40 105

📌 Building a Self-Healing Data Pipeline That Fixes Its Own Python Errors 🗂 Category: LLM APPLICATIONS 🕒 Date: 2026-01-21 | ⏱️ Read time: 8 min read How I built a self-healing pipeline that automatically fixes bad CSVs, schema changes, and weird… #DataScience #AI #Python

40 105

📌 If You Want to Become a Data Scientist in 2026, Do This 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-21 | ⏱️ Read time: 10 min read Learn from my mistakes and fast track your data science career #DataScience #AI #Python

40 105

📌 Google Trends is Misleading You: How to Do Machine Learning with Google Trends Data 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-21 | ⏱️ Read time: 11 min read Google Trends is one of the most widely used tools for analysing human behaviour at… #DataScience #AI #Python

40 105

Repost from Github Top Repositories

🔥 Trending Repository: Data-Science-For-Beginners 📝 Description: 10 Weeks, 20 Lessons, Data Science for All! 🔗 Repository URL: https://github.com/microsoft/Data-Science-For-Beginners 📖 Readme: https://github.com/microsoft/Data-Science-For-Beginners#readme 📊 Statistics: 🌟 Stars: 31.9K stars 👀 Watchers: 513 🍴 Forks: 6.8K forks 💻 Programming Languages: Jupyter Notebook 🏷️ Related Topics:

#python #data_science #pandas #data_visualization #data_analysis #microsoft_for_beginners

================================== 🧠 By: https://t.me/DataScienceM

40 105

🙏💸 500$ FOR THE FIRST 500 WHO JOIN THE CHANNEL! 🙏💸 Join our channel today for free! Tomorrow it will cost 500$! https://t.me/+0-w7MQwkOs02MmJi You can join at this link! 👆👇 https://t.me/+0-w7MQwkOs02MmJi

40 105

📌 How to Perform Large Code Refactors in Cursor 🗂 Category: AGENTIC AI 🕒 Date: 2026-01-20 | ⏱️ Read time: 10 min read Learn how to perform code refactoring with LLMs #DataScience #AI #Python

40 105

📌 Does Calendar-Based Time-Intelligence Change Custom Logic? 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-20 | ⏱️ Read time: 8 min read Let’s look at calculating the moving average over time #DataScience #AI #Python

40 105

📌 Why Package Installs Are Slow (And How to Fix It) 🗂 Category: DATA ENGINEERING 🕒 Date: 2026-01-20 | ⏱️ Read time: 7 min read How sharded indexing patterns solve a scaling problem in package management #DataScience #AI #Python

40 105

📌 You Probably Don’t Need a Vector Database for Your RAG — Yet 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-20 | ⏱️ Read time: 14 min read Numpy or SciKit-Learn might meet all your retrieval needs #DataScience #AI #Python

40 105

📌 Time Series Isn’t Enough: How Graph Neural Networks Change Demand Forecasting 🗂 Category: DATA SCIENCE 🕒 Date: 2026-01-19 | ⏱️ Read time: 11 min read Why modeling SKUs as a network reveals what traditional forecasts miss #DataScience #AI #Python

40 105

📌 Using Local LLMs to Discover High-Performance Algorithms 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-01-19 | ⏱️ Read time: 10 min read How I used open-source models to explore new frontiers in efficient code generation, using my… #DataScience #AI #Python

40 105

📌 Bridging the Gap Between Research and Readability with Marco Hening Tallarico 🗂 Category: AUTHOR SPOTLIGHTS 🕒 Date: 2026-01-19 | ⏱️ Read time: 6 min read Diluting complex research, spotting silent data leaks, and why the best way to learn is… #DataScience #AI #Python