AI and Machine Learning
前往频道在 Telegram
Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more! Buy ads: https://telega.io/c/machine_learning_courses
显示更多📈 Telegram 频道 AI and Machine Learning 的分析概览
频道 AI and Machine Learning (@machine_learning_courses) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 95 141 名订阅者，在 教育 类别中位列第 1 512，并在 印度 地区排名第 3 036 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 95 141 名订阅者。
根据 25 八月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 335，过去 24 小时变化为 -23，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 10.63%。内容发布后 24 小时内通常能获得 2.44% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 10 117 次浏览，首日通常累积 2 325 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 18。
- 主题关注点： 内容集中在 learning, llm, linkedin, linux, udemy 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more!
Buy ads: https://telega.io/c/machine_learning_courses”
凭借高频更新（最新数据采集于 26 八月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 教育 类别中的关键影响点。
95 141
订阅者
-2324 小时
+107 天
+33530 天
数据加载中...
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| 日期 | 订阅者增长 | 提及 | 频道 | |
| 26 八月 | 0 | |||
| 25 八月 | +6 | |||
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| 23 八月 | 0 | |||
| 22 八月 | +24 | |||
| 21 八月 | +4 | |||
| 20 八月 | +27 | |||
| 19 八月 | +28 | |||
| 18 八月 | +24 | |||
| 17 八月 | +13 | |||
| 16 八月 | +17 | |||
| 15 八月 | +5 | |||
| 14 八月 | +24 | |||
| 13 八月 | +39 | |||
| 12 八月 | +8 | |||
| 11 八月 | +23 | |||
| 10 八月 | +19 | |||
| 09 八月 | +1 | |||
| 08 八月 | +36 | |||
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| 04 八月 | +3 | |||
| 03 八月 | +20 | |||
| 02 八月 | +11 | |||
| 01 八月 | +13 |
频道帖子
| 2 |  +5100 AI ML projects for all levels | 2 737 |
| 3 | 📱Artificial intelligence
📱A Content Marketer's Guide to Responsible AI | 4 737 |
| 4 |  🔅 A Content Marketer's Guide to Responsible AI
📝 Learn to use AI responsibly in content marketing, balancing personalization, privacy, and ethical AI practices.
🌐 Author: Lauren Diethelm
🔰 Level: General
⏰ Duration: 23m
📋 Topics: Content Marketing, Artificial Intelligence for Business
🔗 Join Artificial intelligence for more courses | 4 577 |
| 5 |  +1AI Agents vs Agentic AI... what’s the actual difference?
There are 3 types of AI workflows worth knowing and each performs a different task. If you don’t understand these you’re probably falling behind.
Non-Agentic AI:
Basic prompt-response AI with no memory/reasoning.
They’re fast, cheap, and universally accessible, requires no technical build or integration and great for clear, one-off tasks.
Agentic AI:
Self-managing AI system that can plan and execute.
Great for handling complex, changing projects. They can integrate with tools and databases and produce more reliable outcomes.
AI Agent:
A single-task AI worker designed to automate one task.
Automates repetitive, time-consuming tasks, quick setup and cost-efficient and easy to test and refine within roles
In short:
AI Agents = Single-task automation
Agentic AI = Multi-step problem solving | 6 265 |
| 6 |  AI Concepts Explained | 6 732 |
| 7 |  💡 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 sites
🔢 Movies, TV Shows and 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 | 6 111 |
| 8 |  AI Engineer Roadmap | 9 009 |
| 9 | 📱Artificial intelligence
📱Building Agentic AI Systems | 10 014 |
| 10 |  🔅 Building Agentic AI Systems
📝 Gain the knowledge and practical skills required to design and develop an Agentic AI system.
🌐 Author: Rashim Mogha
🔰 Level: Intermediate
⏰ Duration: 1h 2m
📋 Topics: AI Software Development, AI Agents
🔗 Join Artificial intelligence for more courses | 9 888 |
| 11 |  Most Data Scientists structure their projects wrong.
Use this clean, production-ready layout👇
1️⃣ config/ – config files
Separate params from code (local.yaml, prod.yaml)
2️⃣ data/ – full data lifecycle
raw → preprocessed → features → predictions
3️⃣ entrypoint/ – main scripts
train.py (pipeline)
inference.py (batch/real-time)
4️⃣ notebooks/ – exploration only
EDA, analysis — never production logic
5️⃣ src/ – core ML code
feature engineering, training, inference (modular + testable)
6️⃣ tests/ – automated checks
prevent silent failures
7️⃣ docker + env files – reproducibility
same setup on any machine/CI
8️⃣ pinned dependencies – stability
exact versions → consistent results | 9 691 |
| 12 |  +14 stages of LLM Training | 11 038 |
| 13 |  +6Key Nodes in n8n
Most people think AI automation is complex, but with n8n it comes down to just 7 building blocks.
1️⃣ Code Node → custom logic
2️⃣ HTTP Request → connect any API
3️⃣ Edit Fields → clean data
4️⃣ IF Node → conditional paths
5️⃣ Switch Node → handle multiple cases
6️⃣ Loop Over Items → process lists
7️⃣ Error Handling → keep workflows alive
n8n makes it simple: drag, drop, connect. | 13 306 |
| 14 | 📱Artificial intelligence
📱Hands-On AI: Building Your First LLM-Powered App | 11 815 |
| 15 |  🔅 Hands-On AI: Building Your First LLM-Powered App
📝 Get started building apps powered by large language models (LLMs) in this hands-on, skills-based course for beginners.
🌐 Author: Han-chung Lee
🔰 Level: Beginner
⏰ Duration: 1h 14m
📋 Topics: AI Software Development, Large Language Models, Artificial Intelligence
🔗 Join Artificial intelligence for more courses | 11 233 |
| 16 |  All major LLMs, one login: glbgpt.com
GPT-5 · Claude · Gemini · Grok · DeepSeek. Crypto top-up (USDT), pay-as-you-go, no sub required.
→ https://tglink.io/ad3c29e3f95e5e | 3 134 |
| 17 |  📕 The Ultimate Roadmap to master AI Agents | 14 024 |
| 18 |  🔢 Stages of LLM Training
Training a Large Language Model isn’t a single step—it’s a journey through multiple stages that shape how it understands, follows, and reasons.
Here’s the progression:
Stage 0 → A randomly initialized model, just noise with potential.
Stage 1 → Pre-Training, where it absorbs patterns from massive text data.
Stage 2 → Instruction Fine-Tuning, aligning it to follow human-written instructions.
Stage 3 → Preference Fine-Tuning, refining it to generate responses people actually prefer.
Stage 4 → Reasoning Fine-Tuning, pushing it to think more logically and solve complex problems.
From raw parameters to advanced reasoning, each stage transforms the model into something smarter, more helpful, and more aligned with human goals. | 13 197 |
| 19 |  🧠 Code with an AI agent — inside Telegram
BrainDaemon is a private AI coding workspace (Mini App):
• Cloud machine + real tools per chat
• Build multi-file projects, not just paste snippets
• Live reasoning while it works with you
Open the Mini App and start building:
👉 https://t.me/BrainDaemonBot | 2 153 |
| 20 |  🔢 Layers of LLM Stack | 14 280 |
