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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Red:📈 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 68 118 suscriptores, ocupando la posición 2 372 en la categoría Educación y el puesto 4 808 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 68 118 suscriptores.
Según los últimos datos del 27 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 112, y en las últimas 24 horas de 8, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 4.52%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.90% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 3 077 visualizaciones. En el primer día suele acumular 1 291 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 5.
- 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 28 agosto, 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.
68 118
Suscriptores
+824 horas
-727 días
+11230 días
Archivo de publicaciones
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📚 AI / ML / DL Learning Resources Hub 🤖🧠
A structured, end-to-end roadmap to master AI — from fundamentals to cutting-edge research. 🚀
A carefully curated, all-in-one repository designed to help Computer Science students, AI enthusiasts, and professionals 👩💻👨💻 who want to build strong foundations and progress confidently from beginner to advanced levels 📈. This hub brings together the high-quality books 📖, courses 🎓, playlists 🎵, research papers 📝, tools 🛠, and learning roadmaps 🗺 covering: Artificial Intelligence, Machine Learning, Deep Learning, Data Science 📊, Transformers, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and MLOps 🔄, all organized in a clear, practical, and industry-relevant manner.
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Machine Learning Specialization — Study Notes & Labs
Personal notes and lab notebooks from the Machine Learning Specialization by DeepLearning.AI & Stanford Online (Coursera), instructed by Prof. Andrew Ng.
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🔥 Convolutional Neural Networks: Clearly explained!
🖼 Convolutional Neural Networks (CNNs): CNNs belong to the deep learning methods with layers like convolutional, pooling, and fully-connected layers that transform input images for recognition.
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🔄 Training Process: The training involves batches, backpropagation, and gradient descent to minimize errors. The weights start with random values and are updated through backpropagation. This cycle repeats until accuracy is achieved.
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Repost from Learn Python Coding
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