Machine Learning with Python
رفتن به کانال در Telegram
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
نمایش بیشتر
📈 تحلیل کانال تلگرام Machine Learning with Python
کانال Machine Learning with Python (@codeprogrammer) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 68 473 مشترک است و جایگاه 2 398 را در دسته آموزش و رتبه 4 562 را در منطقه الهند دارد.
📊 شاخص‌های مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 68 473 مشترک جذب کرده است.
بر اساس آخرین داده‌ها در تاریخ 01 نوامبر, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -24 و در ۲۴ ساعت گذشته برابر -28 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 3.65% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.44% واکنش نسبت به کل مشترکان کسب می‌کند.
- دسترسی پست‌ها: هر پست به طور میانگین 2 501 بازدید دریافت می‌کند. در اولین روز معمولاً 987 بازدید جمع‌آوری می‌شود.
- واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 8 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند insidead, learning, degree, evaluation, algorithm تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاه‌های شخصی توصیف می‌کند:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 02 نوامبر, 2026)، کانال همواره به‌روز و دارای دسترسی بالاست. تحلیل‌ها نشان می‌دهد مخاطبان به‌طور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته آموزش تبدیل کرده‌اند.
68 473
مشترکین
-2824 ساعت
-1137 روز
-2430 روز
آرشیو پست ها
😉 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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🌟 Join @DeepLearning_ai & @MachineLearning_Programming! 🌟
Explore AI, ML, Data Science, and Computer Vision with us. 🚀
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➡️ @DeepLearning_ai
➡️ @MachineLearning_Programming
Step into the future—today! ✨
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
Repost from Machine Learning with Python
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
🔥 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. 🤖✨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
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!
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)
Free Courses from top companies 🤓
AI (Microsoft) -
https://learn.microsoft.com/en-us/training/paths/get-started-artificial-intelligence/
Deep learning (NVIDIA) -
https://learn.nvidia.com/en-us/training/self-paced-courses
Data science (IBM) -
https://skillsbuild.org/students/course-catalog/data-science
Data Analyst (Microsoft) -
https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/
Python (Microsoft) -
https://learn.microsoft.com/en-us/shows/intro-to-python-development/
Comment
01.07.2025
Publication text
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Remote job search hack shared by a Reddit user who used Google Maps to find companies and reached out directly, avoiding job boards and cover letters. Worth a look: https://www.reddit.com/r/RemoteJobseekers/comments/1fdpeg2/how_i_landed_multiple_remote_job_offers_my_remote/
Repost from Machine Learning
A comprehensive PDF has been compiled that includes all MCP-related posts shared over the past six months.
(75 pages, 10+ projects & visual explainers)
Over the last half year, content has been published about the Modular Computation Protocol (MCP), which has gained significant interest and engagement from the AI community. In response to this enthusiasm, all tutorials have been gathered in one place, featuring:
* The fundamentals of MCP
* Explanations with visuals and code
* 11 hands-on projects for AI engineers
Projects included:
1. Build a 100% local MCP Client
2. MCP-powered Agentic RAG
3. MCP-powered Financial Analyst
4. MCP-powered Voice Agent
5. A Unified MCP Server
6. MCP-powered Shared Memory for Claude Desktop and Cursor
7. MCP-powered RAG over Complex Docs
8. MCP-powered Synthetic Data Generator
9. MCP-powered Deep Researcher
10. MCP-powered RAG over Videos
11. MCP-powered Audio Analysis Toolkit
#MCP #ModularComputationProtocol #AIProjects #DeepLearning #ArtificialIntelligence #RAG #VoiceAI #SyntheticData #AIAgents #AIResearch #TechWriting #OpenSourceAI #AI #python
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk 📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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