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AI and Machine Learning
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Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses
Show more📈 Analytical overview of Telegram channel AI and Machine Learning
Channel AI and Machine Learning (@machine_learning_courses) in the English language segment is an active participant. Currently, the community unites 94 192 subscribers, ranking 1 545 in the Education category and 3 012 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 94 192 subscribers.
According to the latest data from 30 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 965 over the last 30 days and by 57 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 7.33%. Within the first 24 hours after publication, content typically collects 2.71% reactions from the total number of subscribers.
- Post reach: On average, each post receives 6 902 views. Within the first day, a publication typically gains 2 549 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 9.
- Thematic interests: Content is focused on key topics such as learning, llm, linkedin, linux, udemy.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more!
Buy ads: https://telega.io/c/machine_learning_courses”
Thanks to the high frequency of updates (latest data received on 01 July, 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 Education category.
94 192
Subscribers
+5724 hours
+2587 days
+96530 days
Posts Archive
94 192
🔅 Complete Guide to Data Lakes and Lakehouses
📝 Build the foundational knowledge and practical skills essential for data engineers, data scientists, and related professionals to effectively design and utilize data lakes.
🌐 Author: Thalia Barrera
🔰 Level: Advanced
⏰ Duration: 5h 39m
📋 Topics: Data Lakes, Data Engineering, Artificial Intelligence
🔗 Join Artificial intelligence for more courses
94 192
Repost from How AI Helps
Local AI Stack in 2026: what you can actually run on a laptop for text, video, RAG and notebooks
Main point: local AI is no longer a weekend toy. The useful setup is not the biggest model, but the right model for the job and hardware.
🧩 Text: start with
Qwen3-4B/8B, Gemma-3-4B, or Llama-3.2-1B/3B. Qwen3 is neat because it has /think and /no_think: use slower reasoning only when needed. MiMo is worth watching too: Xiaomi's MiMo-7B-RL is on GitHub/HuggingFace, tuned for math, code and reasoning. The paper says the base model used 25T pretraining tokens, then RL on 130K verifiable math/code tasks.
Video: Lightricks/LTX-Video and LTXV-13B can run locally through Python/ComfyUI, but be honest with your laptop. The 13B line wants a serious GPU. For experiments, start with distilled/FP8 or the 2B branch. Lower quality, much faster iteration.
Your docs: local RAG means Chroma or LanceDB, Ollama embeddings like embeddinggemma or qwen3-embedding, then a small LLM. Important detail: use the same embedding model for indexing and search, or the answers will sound smart but miss the source.
Jupyter AI also fits the stack: chat inside JupyterLab, attach files, ask about a notebook or cell, and connect it to local Ollama or vLLM.
⚠️ Hardware note: 16 GB RAM is fine for 1B to 4B quantized models. 32 GB RAM or a discrete GPU makes 7B to 8B much nicer. Long context eats memory fast: Ollama defaults to 4096 tokens, and raising num_ctx hits RAM/VRAM.
Best 2026 laptop stack: small LLM, local embeddings, RAG, Jupyter or IDE integration. You can build it without cloud calls and without a token bill.94 192
🖥 AidLearning
🛠AidLearning is a mobile AI development platform that supports all mainstream development frameworks and tools for deep learning and neural networks.🔰 It has a unique cpu+gpu+npu(dsp) acceleration technology, that brings a significant boost on performance of deep-learning algorithm by the built-in aidlite module. 🔰At the same time, AidLearning also provides developers with popular development tools, such as VSCode and Jupyter Notebook. 🌐 Links: Github
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💡 Welcome to The Premium Vault – Your Gateway to Exclusive Content
🔐 What is The Premium Vault?
We are a private Telegram channel dedicated to delivering high-quality, premium content that you simply cannot find through ordinary searches, free platforms, or standard telegram channels. Every piece of content inside this vault is carefully collected, researched, and created exclusively for our members.📦 What’s Inside? 1⃣ Tutorials, and resources across various premium niches 🔢 Downloadable assets, templates and tools 🔢 Masterpiece Movies and TV Shows 🔢 Legendary Documentaries 🔢 Premium Applications, fully featured, paid-tier software and productivity tools 〰️〰️〰️〰️〰️〰️〰️〰️〰️ 🚫 What You Won't Find Here: No recycled freebies. No low-effort posts. No clickbait. Everything inside The Premium Vault is original, valuable, or rare — shared only with our inner circle of premium subscribers. 🔗 https://t.me/ThePremiumVault/4
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03_AI_Research_and_the_Quest_for_Artificial_General_Intelligence.zip447.31 MB
94 192
🔅 AI for Beginners: Inside Large Language Models
⏲ 3 hours 📁 26 Lessons
📔 Understand how LLMs actually work under the hood from scratch with practical and fun lessons. No prior knowledge required!🎙 Taught by: Scott Kerr 🌟 ZTM Link 📤 Download All Courses
94 192
🔥 Project: fast-agent
fast-agent is a modern framework for rapid development and testing of intelligent agents and workflows supporting the MCP (Model-Context-Protocol) protocol. It provides a simple declarative syntax and powerful tools for building multi-agent systems with support for OpenAI, Anthropic, and other models.
▪️ Main features
• Fast agent creation using @fast.agent decorators, minimizing code amount.
• Workflow support: chains (chain), parallel calls (parallel), routers (router), orchestrators (orchestrator), evaluation and optimization schemes (evaluator_optimizer).
• Multimodality: processing images, PDF files, and integration with external MCP resources.
• Interactive debugging: configuration and diagnostics of agents before, during, and after workflow execution.
• Flexible configuration via fastagent.config.yaml and fastagent.secrets.yaml.
• Integration with LLMs: OpenAI (GPT-4 and others), Anthropic (Haiku, Sonnet, Opus), and other models through MCP servers.
▪️ Quick start
1️⃣ Install the uv package manager for Python.
2️⃣ Install fast-agent:
uv pip install fast-agent-mcp
uv run fast-agent setup
uv run agent.py
uv run fast-agent quickstart workflow
examples directory in the repository.
▪️ Community and development
• The project is actively developed, ⭐️ 1.7k+ stars on GitHub.
• Discussions: [Discussions](https://github.com/evalstate/fast-agent/discussions)
• Latest releases: [Releases](https://github.com/evalstate/fast-agent/releases)
▪️ Video review
[First Look at Fast-Agent (or Manus) – Coding an AI ...](https://www.youtube.com/watch?v=GaVQyYougPc&utm_source=chatgpt.com)
🔍 GitHub94 192
💥 Xiaomi MiMo
🛠Xiaomi Corporation has introduced its first AI model, the compact language model MiMo , which has 7 billion parameters .
🔰It demonstrates performance comparable to GPT o1-mini .
🔰The model code and weights are available on the Hugging Face platform . 🔍
🔰MiMo was trained from scratch in two stages: first for text analysis , and then for solving logic problems , programming and mathematics , using a dataset of 130 thousand tasks . 📊
☝🏻 It is especially worth noting that due to a small number of parameters , MiMo can be run locally . 💻
🔗 Links:
https://github.com/XiaomiMiMo/MiMo
https://huggingface.co/XiaomiMiMo
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🔅 Enhancing Your Notebook Workflow with Jupyter AI
📝 Learn to leverage generative AI capabilities within JupyterLab to enhance your data science and machine learning workflow.
🌐 Author: Wuraola Oyewusi
🔰 Level: Intermediate
⏰ Duration: 1h 10m
📋 Topics: Generative AI Tools, Jupyter, Artificial Intelligence
🔗 Join Artificial intelligence for more courses
