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Machine Learning with Python

Machine Learning with Python

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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

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📈 Análisis del canal de Telegram Machine Learning with Python

El canal Machine Learning with Python (@codeprogrammer) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 67 838 suscriptores, ocupando la posición 2 407 en la categoría Educación y el puesto 5 078 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 67 838 suscriptores.

Según los últimos datos del 04 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 75, y en las últimas 24 horas de 11, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.53%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.84% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 1 717 visualizaciones. En el primer día suele acumular 1 249 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 6.
  • Intereses temáticos: El contenido se centra en temas clave como insidead, learning, degree, evaluation, algorithm.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 05 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 Educación.

67 838
Suscriptores
+1124 horas
+587 días
+7530 días
Archivo de publicaciones

🚀 Demystifying Activation Functions! 🧠✨ Ever wondered why activation functions are so critical in neural networks? 🤔🤖 The
🚀 Demystifying Activation Functions! 🧠✨ Ever wondered why activation functions are so critical in neural networks? 🤔🤖 They’re the secret sauce that allows models to capture complex, nonlinear relationships! 🔥📈 Do you want to learn how to implement an artificial neural network from scratch in Python using NumPy? 🐍📊 Learn more in super-detailed guide: https://lnkd.in/e4CydTtB 🔗📚 #NeuralNetworks #DeepLearning #ActivationFunctions #Python #NumPy #AI

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Most people don’t fail because they lack ambition 🎯 They fail because their time and energy leak into distractions before they ever reach the goal 🕰💔 This visual explains it perfectly: 📊 Time + Energy are only useful when filtered through discipline 🧠🛡 Without discipline, distractions absorb everything: 🌊 • endless notifications 📱 • reactive meetings 🤝 • poor sleep 😴 • stress-driven habits 🤯 • multitasking disguised as productivity 🔄 And in high-performance environments, this becomes a leadership issue, not just a personal one 🏢📉 I see this often in corporate wellness workshops and executive coaching sessions. 🗣 Leaders want better focus, resilience, and performance ⚡️ But the real challenge is not motivation 🚫🔥 It’s protecting their cognitive energy daily 🧠🔒 Discipline is not punishment ⛔️ It’s a system that helps you direct your energy intentionally 🎯 Sometimes that means: ✔️ ✔️ starting the day without your phone 📵 ✔️ eating to stabilize energy and focus 🥗 ✔️ creating recovery moments between meetings ⏸️ ✔️ reducing unnecessary inputs 📉 ✔️ building routines that lower decision fatigue 🧘 Because peak performance is rarely about doing more 🏃 It’s about allowing less distraction to consume what matters most 🎯✨ Your goals are not only built by effort 💪 They are built by what you consistently refuse to give your energy to 🚫🕸 And if you’re ready to start, I created something simple for you: 🚀 My 7 Days to Peak Performance email series 📧 designed to help you improve your energy, focus, and productivity with practical daily strategies you can actually stick to. 📅✅ You can join here: 🔗 https://lnkd.in/eA3h9wb8 #PeakPerformance #ProductivityHacks #FocusMastery #Discipline #ExecutiveCoaching #MindsetShift

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🔖 Interactive textbook on probability theory and statistics 📊✨ A super-intuitive site where you can visually study distributions, sampling, and statistical concepts. 📈🎲 No tons of formulas and boring theory — everything is demonstrated through interactive examples and simulations. 💻🔬 ⛓️ Download here 👇 https://seeing-theory.brown.edu/ #Probability #Statistics #DataScience #Learning #Interactive #Math https://t.me/CodeProgrammer

14 minutes with an Anthropic engineer will teach you more about building agents 🤖 than most devs figure out in months of trial and error 🛠. Same guy who wrote “Building Effective Agents”, the post every AI builder has bookmarked 📑. No fluff. No 47-tool frameworks. Just the patterns that actually work in production 🚀: → When to use workflows vs. agents (most people get this wrong) ❌ → Why simple > clever, every single time ✅ → The orchestrator-worker pattern that scales 📈 → When NOT to build an agent at all 🛑 If you’re shipping AI products in 2026 and haven’t watched this, you’re doing it on hard mode 🎮. 14 minutes. Bookmark it 📌. Watch it twice 👀. #AI #Agents #Tech #DevCommunity #FutureTech #ProgrammingConcepts

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Repost from Machine Learning
Google Gemma 4's pre-training is completely free All you need is a browser and access to more than 500 models to choose from. The process is simple: 1. Open the notebook of Unsloth in Colab 2. Select a model and a dataset 3. Start the trainin Link: https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb It's done 😂 👉 https://t.me/MachineLearning9