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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 40 150 suscriptores, ocupando la posición 3 364 en la categoría Tecnologías y Aplicaciones y el puesto 227 en la región Siria.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 150 suscriptores.
Según los últimos datos del 27 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 412, y en las últimas 24 horas de 5, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.96%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.89% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 785 visualizaciones. En el primer día suele acumular 760 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 2.
- 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 28 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.
40 150
Suscriptores
+524 horas
+1067 días
+41230 días
Archivo de publicaciones
40 154
📌 The Trap of Sprints: Don’t Be Like Scarlett O’Hara. Think Today!
🗂 Category: AGILE
🕒 Date: 2024-06-03 | ⏱️ Read time: 11 min read
Why data scientists should prioritize communication and flexibility in agile projects
40 154
📌 A Deep Dive into Fine-Tuning
🗂 Category: NATURAL LANGUAGE PROCESSING
🕒 Date: 2024-06-03 | ⏱️ Read time: 30 min read
Stepping out of the “comfort zone” – part 3/3 of a deep-dive into domain adaptation…
40 154
📌 The Meaning of Explainability for AI
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2024-06-04 | ⏱️ Read time: 10 min read
Do we still care about how our machine learning does what it does?
40 154
📌 Understanding You Only Cache Once
🗂 Category:
🕒 Date: 2024-06-04 | ⏱️ Read time: 11 min read
This blog post will go in detail on the “You Only Cache Once: Decoder-Decoder Architectures…
40 154
📌 The Math Behind Gated Recurrent Units
🗂 Category: DEEP LEARNING
🕒 Date: 2024-06-04 | ⏱️ Read time: 33 min read
Dive into advanced deep learning with gated recurrent units (GRUs), understand their mathematics, and implement…
40 154
📌 Effective Strategies for Managing ML Initiatives
🗂 Category: DATA SCIENCE
🕒 Date: 2024-06-04 | ⏱️ Read time: 7 min read
Embracing uncertainty, right people, and learning from the data
40 154
📌 Data Disruptions to Elevate Entity Embeddings
🗂 Category: MACHINE LEARNING
🕒 Date: 2024-06-04 | ⏱️ Read time: 15 min read
Injecting random values during neural network training can help you get more from your categoricals
40 154
📌 Predicting Chicago Taxi Trips with R Time Series Model – BSTS
🗂 Category: DATA SCIENCE
🕒 Date: 2024-06-04 | ⏱️ Read time: 9 min read
Step-by-step tutorial on how to forecast number of taxi trips using R time series model
40 154
📌 How to Build Guardrails for Effective Agents
🗂 Category: LLM APPLICATIONS
🕒 Date: 2025-10-19 | ⏱️ Read time: 7 min read
Learn how to set up effective guardrails to enforce desired behaviour from your agents
40 154
📌 Conceptual Frameworks for Data Science Projects
🗂 Category: DATA SCIENCE
🕒 Date: 2025-10-19 | ⏱️ Read time: 18 min read
An overview of common framework types and a simple process for building custom frameworks
40 154
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40 154
📌 “Sparks of Chemical Intuition”-and Gross Limitations!-in AlphaFold 3
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2024-06-05 | ⏱️ Read time: 20 min read
Observations after 3 weeks of DeepMind releasing its hitherto most advanced model for biomolecular structure…
40 154
📌 Slicing in Python: A Comprehensive Guide
🗂 Category: DATA SCIENCE
🕒 Date: 2024-06-05 | ⏱️ Read time: 32 min read
Master Python slicing: from lists and tuples to NumPy arrays and Pandas dataframes, plus custom…
40 154
🤖🧠 Unleashing the Power of AI with Open Agent Builder: A Visual Workflow Tool for AI Agents
🗓️ 19 Oct 2025
📚 AI News & Trends
In today’s rapidly advancing technological landscape, artificial intelligence (AI) is not just a buzzword, it’s a transformative force across industries. From automating complex tasks to streamlining operations, AI is revolutionizing workflows. However, designing and deploying AI-driven workflows has traditionally required expert-level programming knowledge. Enter Open Agent Builder, a revolutionary tool that democratizes the creation of ...
#AI #ArtificialIntelligence #OpenAgentBuilder #AIAgents #VisualWorkflow #TechInnovation
40 154
📌 The One Billion Row Challenge in Julia
🗂 Category: DATA SCIENCE
🕒 Date: 2024-06-05 | ⏱️ Read time: 9 min read
What can data scientists learn should they choose to accept this mission?
40 154
📌 The 5 Data Science Skills You Can’t Ignore in 2024
🗂 Category: CAREER ADVICE
🕒 Date: 2024-06-05 | ⏱️ Read time: 18 min read
Boost your career with these essential data science skills
40 154
📌 Business Planning with Python – Inventory and Cash Flow Management
🗂 Category: ANALYTICS
🕒 Date: 2024-06-05 | ⏱️ Read time: 15 min read
Business planning of small businesses to manage inventory, predict liquidity needs and maximize profitability with…
40 154
📌 How to Deploy ML Solutions with FastAPI, Docker, and GCP
🗂 Category: DATA SCIENCE
🕒 Date: 2024-06-05 | ⏱️ Read time: 11 min read
A hands-on guide with Python example code
40 154
📌 Solving a Resource Planning Problem with Mathematical Programming and Column Generation
🗂 Category: DATA SCIENCE
🕒 Date: 2024-06-05 | ⏱️ Read time: 25 min read
Solving the minimum vertex coloring problem via column generation
40 154
📌 Why do Computers even use Binary?
🗂 Category: DATA SCIENCE
🕒 Date: 2024-06-05 | ⏱️ Read time: 9 min read
A Budding Data Scientist’s Introduction to Computer Hardware
