Artificial Intelligence

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🔒 Welcome Artificial Intelligence Channel Buy ads: https://telega.io/c/Artificial_Intelligence_COM

Network:Zero To Mastery India4 353 Technologies & Applications1 739...

📈 Analytical overview of Telegram channel Artificial Intelligence

Channel Artificial Intelligence (@artificial_intelligence_com) in the English language segment is an active participant. Currently, the community unites 72 370 subscribers, ranking 1 739 in the Technologies & Applications category and 4 353 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 72 370 subscribers.

According to the latest data from 01 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 309 over the last 30 days and by -41 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 6.58%. Within the first 24 hours after publication, content typically collects 1.88% reactions from the total number of subscribers.
Post reach: On average, each post receives 4 759 views. Within the first day, a publication typically gains 1 361 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 14.
Thematic interests: Content is focused on key topics such as learning, linkedin, linux, udemy, 040k|.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“🔒 Welcome Artificial Intelligence Channel Buy ads: https://telega.io/c/Artificial_Intelligence_COM”

Thanks to the high frequency of updates (latest data received on 02 September, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

72 370

Subscribers

-4124 hours

-1617 days

+30930 days

4 759

Post views

~ 1 36124 hours

~ 1 90648 hours

6.58%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

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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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🔗 Machine learning project ideas

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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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💡 Different between Data science vs AI vs ML

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AI tools for online business

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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