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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 PythonTarmoq:Machine Learning with PythonHindiston5 038Taʼlim2 416

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Machine Learning with Python (@codeprogrammer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 67 813 obunachidan iborat bo'lib, Taʼlim toifasida 2 416-o'rinni va Hindiston mintaqasida 5 038-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 67 813 obunachiga ega bo‘ldi.

09 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 70 ga, so‘nggi 24 soatda esa 10 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 2.94% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.44% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 1 997 marta ko‘riladi; birinchi sutkada odatda 1 652 ta ko‘rish yig‘iladi.
  • Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 7 ta reaksiya keladi.
  • Tematik yo‘nalishlar: Kontent insidead, learning, degree, evaluation, algorithm kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 10 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

67 813
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Postlar arxiv
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
Tired of endless job boards and low offers? Unlock access to exclusive remote jobs from top startups—some with salaries $100k
Tired of endless job boards and low offers? Unlock access to exclusive remote jobs from top startups—some with salaries $100k+ and early-bird roles at $50/h and above. New high-paying openings posted daily—tech, marketing, design, and more. Ready to upgrade your career from anywhere? Check today’s top jobs now before they’re gone! #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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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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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

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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?
#DataScience #SQL #Python #MachineLearning #Statistics #BusinessAnalytics #ProductCaseStudies #DataScienceProjects #InterviewPrep #LearnDataScience #YouTubeLearning #CodingInterview #MLInterview #SQLProjects #PythonForDataScience
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk

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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

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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
❗️ JAY HELPS EVERYONE EARN MONEY!$29,000 HE'S GIVING AWAY TODAY! Everyone can join his channel and make money! He gives away from $200 to $5.000 every day in his channel https://t.me/+LgzKy2hA4eY0YWNl ⚡️FREE ONLY FOR THE FIRST 500 SUBSCRIBERS! FURTHER ENTRY IS PAID! 👆👇 https://t.me/+LgzKy2hA4eY0YWNl

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.
#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk

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
🔥 The coolest AI bot on Telegram 💢 Completely free and knows everything, from simple questions to complex problems. ☕️ Helps you with anything in the easiest and fastest way possible. ♨️ You can even choose girlfriend or boyfriend mode and chat as if you’re talking to a real person 😋 💵 Includes weekly and monthly airdrops!❗️ 😵‍💫 Bot ID: @chatgpt_officialbot 💎 The best part is, even group admins can use it right inside their groups! ✨ 📺 Try now: • Type FunFact! for a jaw-dropping AI trivia. • Type RecipePlease! for a quick, tasty meal idea. • Type JokeTime! for an instant laugh. Or just say Surprise me! and I'll pick something awesome for you. 🤖✨

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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
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk 📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Machine Learning with Python
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You can now send messages or buy ads in our channel

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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)