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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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Machine Learning with PythonRed:Machine Learning with PythonIndia5 038Educación2 416

📈 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 67 813 suscriptores, ocupando la posición 2 416 en la categoría Educación y el puesto 5 038 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 67 813 suscriptores.

Según los últimos datos del 09 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 70, y en las últimas 24 horas de 10, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.94%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.44% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 1 997 visualizaciones. En el primer día suele acumular 1 652 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 7.
  • 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 10 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.

67 813
Suscriptores
+1024 horas
+127 días
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Archivo de publicaciones
Machine Learning with Python
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Repost from Machine Learning
Looking for a $10k–$15k/month remote job? Top international startups post new offers DAILY. Land high-paying roles in tech, m
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Repost from AI & ML Papers
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Repost from Python Courses & Resources
Tired of endless job hunting? Unlock high-paying remote jobs from top startups – fresh roles posted daily. Want early access
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Repost from Python Courses & Resources
Tired of endless job hunting? Unlock high-paying remote jobs from top startups – fresh roles posted daily. Want early access
Tired of endless job hunting? Unlock high-paying remote jobs from top startups – fresh roles posted daily. Want early access to exclusive $50+/h positions you won’t find on LinkedIn? Get ahead now — the best offers go fast! See today’s hottest openings before everyone else. #إعلان InsideAds

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Repost from Python Courses & Resources
Tired of endless job hunting? Unlock high-paying remote jobs from top startups – fresh roles posted daily. Want early access
Tired of endless job hunting? Unlock high-paying remote jobs from top startups – fresh roles posted daily. Want early access to exclusive $50+/h positions you won’t find on LinkedIn? Get ahead now — the best offers go fast! See today’s hottest openings before everyone else. #إعلان InsideAds

Machine Learning with Python
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t.me/PythonArab arab group about python and ML

Machine Learning with Python
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Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visua
This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

Machine Learning with Python
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NUMPY FOR DS.pdf4.50 MB

Machine Learning with Python
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😉 A list of the best YouTube videos ✅ To learn data science 1️⃣ SQL language ⬅️ Learning 💰 4-hour SQL course from zero to one hundred 💰 Window functions tutorial ⬅️ Projects 📎 Starting your first SQL project 💰 Data cleansing project 💰 Restaurant order analysis ⬅️ Interview 💰 How to crack the SQL interview? ➖➖➖ 2️⃣ Python ⬅️ Learning 💰 12-hour Python for Data Science course ⬅️ Projects 💰 Python project for beginners 💰 Analyzing Corona Data with Python ⬅️ Interview 💰 Python interview golden tricks 💰 Python Interview Questions ➖➖➖ 3️⃣ Statistics and machine learning ⬅️ Learning 💰 7-hour course in applied statistics 💰 Machine Learning Training Playlist ⬅️ Projects 💰 Practical ML Project ⬅️ Interview 💰 ML Interview Questions and Answers 💰 How to pass a statistics interview? ➖➖➖ 4️⃣ Product and business case studies ⬅️ Learning 💰 Building strong product understanding 💰 Product Metric Definition ⬅️ Interview 💰 Case Study Analysis Framework 💰 How to shine in a business interview?
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What is torch.nn really? When I started working with PyTorch, my biggest question was: "What is torch.nn?". This article expl
What is torch.nn really?
When I started working with PyTorch, my biggest question was: "What is torch.nn?".
This article explains it quite well. 📌 Read

Machine Learning with Python
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❗️ JAY HELPS EVERYONE EARN MONEY!$29,000 HE'S GIVING AWAY TODAY! Everyone can join his channel and make money! He gives away
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Machine Learning with Python
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🌟 Join @DeepLearning_ai & @MachineLearning_Programming! 🌟 Explore AI, ML, Data Science, and Computer Vision with us. 🚀 💡
🌟 Join @DeepLearning_ai & @MachineLearning_Programming! 🌟 Explore AI, ML, Data Science, and Computer Vision with us. 🚀 💡 Stay Updated: Latest trends & tutorials. 🌐 Grow Your Network: Engage with experts. 📈 Boost Your Career: Unlock tech mastery. Subscribe Now! ➡️ @DeepLearning_ai ➡️ @MachineLearning_Programming Step into the future—today! ✨

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GPU by hand ✍️ I drew this to show how a GPU speeds up an array operation of 8 elements in parallel over 4 threads in 2 clock cycles. Read more 👇 CPU • It has one core. • Its global memory has 120 locations (0-119). • To use the GPU, it needs to copy data from the global memory to the GPU. • After GPU is done, it will copy the results back. GPU • It has four cores to run four threads (0-3). • It has a register file of 28 locations (0-27) • This register file has four banks (0-3). • All threads share the same register file. • But they must read/write using the four banks. • Each bank allows 2 reads (Read 0, Read 1) and 1 write in a single clock cycle.
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Machine Learning with Python
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Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visua
This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

Machine Learning with Python
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🔥 The coolest AI bot on Telegram 💢 Completely free and knows everything, from simple questions to complex problems. ☕️ Help
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Introduction to Deep Learning As we continue to push the boundaries of what's possible with artificial intelligence, I wanted to take a moment to share some insights on one of the most exciting fields in AI: Deep Learning. Deep Learning is a subset of machine learning that uses neural networks to analyze and interpret data. These neural networks are designed to mimic the human brain, with layers of interconnected nodes (neurons) that process and transmit information. What makes Deep Learning so powerful? Ability to learn from large datasets: Deep Learning algorithms can learn from vast amounts of data, including images, speech, and text. Improved accuracy: Deep Learning models can achieve state-of-the-art performance in tasks such as image recognition, natural language processing, and speech recognition. Ability to generalize: Deep Learning models can generalize well to new, unseen data, making them highly effective in real-world applications. Real-world applications of Deep Learning Computer Vision: Self-driving cars, facial recognition, object detection Natural Language Processing: Language translation, text summarization, sentiment analysis Speech Recognition: Virtual assistants, voice-controlled devices. #DeepLearning #AI #MachineLearning #NeuralNetworks #ArtificialIntelligence #DataScience #ComputerVision #NLP #SpeechRecognition #TechInnovation
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You can now send messages or buy ads in our channel

Machine Learning with Python
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Machine Learning with Python
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This GitHub Repo will be very helpful if you are preparing for a data science technical interview. This question bank covers:
This GitHub Repo will be very helpful if you are preparing for a data science technical interview. This question bank covers: 1️⃣ Machine Learning Interview Questions & Answers 2️⃣ Deep Learning Interview Questions & Answers 2.1. Deep learning basics 2.2. Deep learning for computer vision questions 2.3. Deep learning for NLP & LLMs 3️⃣ Probability Interview Questions & Answers 4️⃣ Statistics Interview Questions & Answers 5️⃣ SQL Interview Questions & Answers 6️⃣ Python Questions & Answers ⚡ You can find the repo link in the comments section!

Machine Learning with Python
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Auto-Encoder & Backpropagation by hand ✍️ lecture video ~ 📺 https://byhand.ai/cv/10 It took me a few years to invent this me
Auto-Encoder & Backpropagation by hand ✍️ lecture video ~ 📺 https://byhand.ai/cv/10 It took me a few years to invent this method to show both forward and backward passes for a non-trivial case of a multi-layer perceptron over a batch of inputs, plus gradient descents over multiple epochs, while being able to hand calculate each step and code in Excel at the same time. = Chapters = • Encoder & Decoder (00:00) • Equation (10:09) • 4-2-4 AutoEncoder (16:38) • 6-4-2-4-6 AutoEncoder (18:39) • L2 Loss (20:49) • L2 Loss Gradient (27:31) • Backpropagation (30:12) • Implement Backpropagation (39:00) • Gradient Descent (44:30) • Summary (51:39)