Artificial Intelligence
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شبکه:📈 تحلیل کانال تلگرام Artificial Intelligence
کانال Artificial Intelligence (@artificial_intelligence_com) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 72 399 مشترک است و جایگاه 1 724 را در دسته فناوری و برنامهها و رتبه 4 344 را در منطقه الهند دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 72 399 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 31 اوت, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 363 و در ۲۴ ساعت گذشته برابر -19 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 6.53% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.94% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 4 727 بازدید دریافت میکند. در اولین روز معمولاً 1 407 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 13 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند learning, linkedin, linux, udemy, 040k| تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“🔒 Welcome Artificial Intelligence Channel
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به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 01 سپتامبر, 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 فناوری و برنامهها تبدیل کردهاند.
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📱Artificial Intelligence and Machine Learning
📱High-Performance PySpark: Advanced Strategies for Optimal Data Processing
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📂 Full description
Master the art of efficient data processing with this advanced PySpark course designed for data engineers. Instructor Ameena Ansari shows you the essentials of optimizing the data cleaning process and defining schemas to streamline ingestion at scale. Explore various data formats and compression techniques to ensure seamless performance, even with massive datasets. By the end of this course, you'll have the tools and skills you need to transform and ingest high-quality data using PySpark pipelines that are both scalable and efficient.This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that youll likely encounter in the workplace. Check out “Using GitHub Codespaces" with this course to learn how to get started.
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🔅 High-Performance PySpark: Advanced Strategies for Optimal Data Processing
🌐 Author: Ameena Ansari
🔰 Level: Advanced
⏰ Duration: 1h 22m
🌀 Discover techniques for optimizing data cleaning, selecting efficient data formats, minimizing shuffling and skew, and performing high-performance data processing at scale.📗 Topics: Data Pipelines, PySpark, Data Processing 📤 Join Artificial Intelligence and Machine Learning for more courses
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🔗 30 Useful AI Apps That Can Help You in 2025
AI apps are taking over the world. There’s an AI app for every conceivable use case. Here are some AI apps for different categories:1 - General Purpose: Perplexity, Anthropic Claude, Grok, ChatGPT, and Gemini 2 - Writing Code: Cursor, Replit, Windsurf AI, Github Copilot, and Tabnine 3 - Productivity: Adobe (PDF Chat), Gemini for Gmail, Gamma (AI slide deck), WisprFlow (AI voice dictation), and Granola (AI notetaker) 4 - Audience Building: Delphi (AI text, voice), HeyGen (video translation), Persona (AI agent builder), Captions (AI video editing), and OpusClips (Video repurposing) 5 - Creativity: ElevenLabs (realistic AI voices), Midjourney, Suno AI (music generation), Krea (enhance images), and Photoroom (AI image editing) 6 - Learning and Growth: Particle News App, Rosebud (AI journal app), NotebookLM, GoodInside (parenting co-pilot), and Ash (AI counselor).
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📱Artificial Intelligence and Machine Learning
📱Artificial Intelligence Foundations: Machine Learning
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📂 Full description
Machine learning is the most exciting branch of artificial intelligence. It allows systems to learn from data by identifying patterns and making decisions with little to no human intervention. In this course, you'll navigate the machine learning lifecycle by getting hands-on practice training your first machine learning model. Join instructor Kesha Williams as she explores widely adopted machine learning methods: supervised, unsupervised, and reinforcement. There's a focus on sourcing and preparing data and selecting the best learning algorithm for your project. After training a model, learn to evaluate model performance using standard metrics. Finally, Kesha shows you how to streamline the process by building a machine learning pipeline. If youre looking to understand the machine learning lifecycle and the steps required to build systems, check out this course.
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🔅 Artificial Intelligence Foundations: Machine Learning
🌐 Author: Kesha Williams
🔰 Level: Beginner
⏰ Duration: 1h 50m
🌀 Learn about the machine learning lifecycle and the steps required to build systems in this hands-on course.📗 Topics: Machine Learning, Artificial Intelligence 📤 Join Artificial Intelligence and Machine Learning for more courses
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8. Set up the user interface and trigger the main function.
• Provides an input field for the user's question
• Triggers the main function when the user clicks "Get Answer"
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7. Define the main function to run all LLMs and aggregate results.
• Runs all reference models asynchronously
• Displays individual responses in expandable sections
• Aggregates responses using the aggregator model
• Streams the aggregated response.
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6. Implement the LLM call function.
• Asynchronously calls the LLM with the user's prompt
• Returns the model name and its response
