Machine Learning
前往频道在 Telegram
Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho
显示更多📈 Telegram 频道 Machine Learning 的分析概览
频道 Machine Learning (@machinelearning9) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 40 057 名订阅者,在 技术与应用 类别中位列第 3 402,并在 叙利亚 地区排名第 232 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 40 057 名订阅者。
根据 22 六月, 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 372,过去 24 小时变化为 2,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 1.94%。内容发布后 24 小时内通常能获得 1.16% 的反应,占订阅者总量。
- 帖子覆盖: 每篇帖子平均可获得 775 次浏览,首日通常累积 466 次浏览。
- 互动与反馈: 受众积极参与,单帖平均反应数为 3。
- 主题关注点: 内容集中在 distance, insidead, gpu, learning, degree 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:
“Real Machine Learning — simple, practical, and built on experience.
Learn step by step with clear explanations and working code.
Admin: @HusseinSheikho || @Hussein_Sheikho”
凭借高频更新(最新数据采集于 23 六月, 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 技术与应用 类别中的关键影响点。
40 057
订阅者
+224 小时
+237 天
+37230 天
帖子存档
40 070
📌 Building Robust Credit Scoring Models (Part 3)
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-03-20 | ⏱️ Read time: 18 min read
Handling outliers and missing values in borrower data using Python.
#DataScience #AI #Python
40 070
📌 The Basics of Vibe Engineering
🗂 Category: AGENTIC AI
🕒 Date: 2026-03-19 | ⏱️ Read time: 14 min read
Building products without the coding part
#DataScience #AI #Python
40 070
📌 Vibe Coding with AI: Best Practices for Human-AI Collaboration in Software Development
🗂 Category: AGENTIC AI
🕒 Date: 2026-03-19 | ⏱️ Read time: 16 min read
Accelerate coding with AI while staying in control and building reliable, production-ready software.
#DataScience #AI #Python
40 070
📌 Linear Regression Is Actually a Projection Problem, Part 1: The Geometric Intuition
🗂 Category: DATA SCIENCE
🕒 Date: 2026-03-19 | ⏱️ Read time: 14 min read
A visual guide to vectors and projections
#DataScience #AI #Python
40 070
📌 Beyond Prompt Caching: 5 More Things You Should Cache in RAG Pipelines
🗂 Category: AGENTIC AI
🕒 Date: 2026-03-19 | ⏱️ Read time: 13 min read
A practical guide to caching layers across the RAG pipeline, from query embeddings to full…
#DataScience #AI #Python
40 070
Repost from Machine Learning with Python
PhD Students - Do you need datasets for your research?
Here are 30 datasets for research from NexData.
Use discount code for 20% off: G5W924C3ZI
1. Korean Exam Question Dataset for AI Training
https://lnkd.in/d_paSwt7
2. Multilingual Grammar Correction Dataset
https://lnkd.in/dV43iqTp
3. High quality video caption dataset
https://lnkd.in/dY9kxkhx
4. 3D models and scenes datasets for AI and simulation
https://lnkd.in/dT-zscH4
5. Image editing datasets – object removal, addition & modification
https://lnkd.in/dd8iCGMS
6. QA dataset – visual & text reasoning
https://lnkd.in/dc3TNWFD
7. English instruction tuning dataset
https://lnkd.in/dTeTgd2M
8. Large scale vision language dataset for AI training
https://lnkd.in/dBJuxazN
9. News dataset
https://lnkd.in/dYBJe5gd
10. Global building photos dataset
https://lnkd.in/dVJsDXnC
11. Facial landmarks dataset
https://lnkd.in/dz_KGCS4
12. 3D Human Pose & Landmarks dataset
https://lnkd.in/dXE9ir8Z
13. 3D Hand Pose & Gesture Recognition dataset
https://lnkd.in/d_QdGGb9
14. 14. Driver monitoring dataset – dangerous, fatigue
https://lnkd.in/d6kF-9PW
15. Japanese handwriting OCR dataset
https://lnkd.in/dHnriqrH
16. American English Male voice TTS dataset
https://lnkd.in/dqyvg862
17. Riddles and brain teasers dataset
https://lnkd.in/dKBHY3DE
18. Chinese test questions text
https://lnkd.in/dQpUd8xC
19. Chinese medical question answering data
https://lnkd.in/dsbWUCpz
20. Multi-round interpersonal dialogues text data
https://lnkd.in/dQiUq_Jg
21. Human activity recognition dataset
https://lnkd.in/dHM52MfV
22. Facial expression recognition dataset
https://lnkd.in/dqQAfMau
23. Urban surveillance dataset
https://lnkd.in/dc2RCnTk
24. Human body segmentation dataset
https://lnkd.in/d6sSrDxS
25. Fashion segmentation – clothing & accessories
https://lnkd.in/dptNUTz8
26. Fight video dataset – action recognition
https://lnkd.in/dnY_m5hZ
27. Gesture recognition dataset
https://lnkd.in/dFVPivYg
28. Facial skin defects dataset
https://lnkd.in/dKCbUvU6
29. Smoke detection and behaviour recognition dataset
https://lnkd.in/ddGg56R4
30. Weight loss transformation video dataset
https://lnkd.in/dqqT4ed9
https://t.me/CodeProgrammer 👾
40 070
📌 Why You Should Stop Worrying About AI Taking Data Science Jobs
🗂 Category: DATA SCIENCE
🕒 Date: 2026-03-18 | ⏱️ Read time: 8 min read
It’s all just fearmongering
#DataScience #AI #Python
40 070
📌 The New Experience of Coding with AI
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-03-18 | ⏱️ Read time: 12 min read
The seduction of AI code assistants
#DataScience #AI #Python
40 070
Repost from Machine Learning with Python
Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
40 070
📌 Two-Stage Hurdle Models: Predicting Zero-Inflated Outcomes
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-03-18 | ⏱️ Read time: 20 min read
Why one model can’t do two jobs
#DataScience #AI #Python
40 070
Listen, here’s the crazy part: while everyone’s scared BTC might crash, insiders just dropped $350 MILLION buying the dip. Unreal, right? The market’s playing a game where the biggest shorts might implode first-like a loaded gun cocked and ready. This isn’t hype, it’s cold, hard data from 14 years of trading wisdom. Wanna see how the pros move and actually win? Check this out 👉 Scalping Kings No fluff, just profits.
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40 070
CNN vs Vision Transformer — The Battle for Computer Vision 👁⚡️
Two architectures. One goal: identify the cat. But they see things differently:
🧠 CNN (Convolutional Neural Network)
· Scans the image with filters
· Detects local patterns first (edges → textures → shapes)
· Builds understanding layer by layer
🔄 Vision Transformer (ViT)
· Splits image into patches (like words in a sentence)
· Detects global patterns from the start
· Sees the whole picture using attention mechanisms
Same input. Same output. Different journey.
CNNs think locally and build up.
Transformers think globally from the get-go.
Which one wins? Depends on the task — but both are shaping the future of how machines see.
https://t.me/CodeProgrammer
40 070
📌 Self-Hosting Your First LLM
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-03-17 | ⏱️ Read time: 20 min read
Privacy. Cost. Customization. Everything you need to know—step by step.
#DataScience #AI #Python
40 070
📌 How to Effectively Review Claude Code Output
🗂 Category: AGENTIC AI
🕒 Date: 2026-03-17 | ⏱️ Read time: 7 min read
Get more out of your coding agents by making reviewing more efficient
#DataScience #AI #Python
40 070
Time Complexity of 10 Most Popular ML Algorithms Know What You're Waiting For ⏳🧠
40 070
Repost from Machine Learning with Python
🚀 𝐓𝐎𝐏 𝐑𝐀𝐆 𝐈𝐍𝐓𝐄𝐑𝐕𝐈𝐄𝐖 𝐐𝐔𝐄𝐒𝐓𝐈𝐎𝐍𝐒 𝐀𝐍𝐃 𝐀𝐍𝐒𝐖𝐄𝐑𝐒
🔹 Advanced #RAG engineering concepts
• Multi-stage retrieval pipelines
• Agentic RAG vs classical RAG
• Latency optimization
• Security risks in enterprise RAG systems
• Monitoring and debugging production RAG systems
📄 𝐓𝐡𝐞 𝐏𝐃𝐅 𝐜𝐨𝐧𝐭𝐚𝐢𝐧𝐬 𝟒𝟎 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐰𝐢𝐭𝐡 𝐜𝐥𝐞𝐚𝐫 𝐞𝐱𝐩𝐥𝐚𝐧𝐚𝐭𝐢𝐨𝐧𝐬 𝐭𝐨 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐛𝐨𝐭𝐡 𝐜𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐚𝐧𝐝 𝐬𝐲𝐬𝐭𝐞𝐦 𝐝𝐞𝐬𝐢𝐠𝐧 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠.
https://t.me/CodeProgrammer
40 070
📌 How a Neural Network Learned Its Own Fraud Rules: A Neuro-Symbolic AI Experiment
🗂 Category: DEEP LEARNING
🕒 Date: 2026-03-17 | ⏱️ Read time: 18 min read
Most neuro-symbolic systems inject rules written by humans. But what if a neural network could…
#DataScience #AI #Python
40 070
📌 Introducing Gemini Embeddings 2 Preview
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-03-17 | ⏱️ Read time: 10 min read
One embedding model to rule them all
#DataScience #AI #Python
40 070
📌 How to Build a Production-Ready Claude Code Skill
🗂 Category: AGENTIC AI
🕒 Date: 2026-03-16 | ⏱️ Read time: 11 min read
What I learned building and distributing my first Skill from scratch
#DataScience #AI #Python
40 070
📌 Follow the AI Footpaths
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-03-16 | ⏱️ Read time: 6 min read
Shadow AI and the desire paths of modern work
#DataScience #AI #Python
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