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 68 550 suscriptores, ocupando la posición 2 402 en la categoría Educación y el puesto 4 696 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 68 550 suscriptores.
Según los últimos datos del 27 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 119, 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 3.75%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.56% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 572 visualizaciones. En el primer día suele acumular 1 068 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 8.
- 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 octubre, 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.
68 550
Suscriptores
-924 horas
-597 días
+11930 días
Archivo de publicaciones
🎓 Thesis • Dissertation • Research • Programming • Simulation
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🎓 Thesis • Dissertation • Research • Programming • Simulation
From a single research idea…
to a complete academic masterpiece.
🔹 Professional assistance for:
✔️ Master’s & PhD Theses
✔️ ISI / Scopus Articles
✔️ Research Proposals & Methodology
✔️ Data Analysis & Statistical Modeling
✔️ AI & Machine Learning Projects
✔️ MATLAB • Python • Simulink • Abaqus • COMSOL • Ansys • ETAP • PSCAD • HOMER • Proteus • LabVIEW
✔️ Electrical, Civil, Mechanical, Medical, Management, Computer Science & All Engineering Fields
✔️ Rare & High-Quality Datasets
✔️ Simulation Projects & Optimization Algorithms
✔️ Academic Presentation Design
✔️ Journal Revision & Reviewer Response Preparation
📊 Accurate Results
📚 Professional Documentation
💻 Clean & Structured Coding
🔒 Full Confidentiality
⏳ On-Time Delivery
Your research deserves more than copy-paste work.
It deserves precision, originality, and engineering-level thinking.
✨ Turning complex ideas into publishable research.
📩 Contact us for consultation and project evaluation.
https://t.me/Omidyzd62
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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.
The resources are selected to balance theory 🧠, intuition 💡, and real-world application 🌍, allowing learners to follow modules sequentially or in parallel ⏳ based on their goals.
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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.
📂 Repo: https://github.com/TruongDat05/machine-learning-notes-and-code
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🔥 Convolutional Neural Networks: Clearly explained!
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Repost from Learn Python Coding
Master Python the Right Way – Without Procrastination. 🐍✨
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