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

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网络:Machine Learning with Python 叙利亚232 技术与应用3 398...

📈 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 40 100 名订阅者，在 技术与应用 类别中位列第 3 398，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 40 100 名订阅者。

根据 23 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 379，过去 24 小时变化为 30，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 1.92%。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 770 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 24 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。

40 100

订阅者

+3024 小时

+337 天

+37930 天

770

帖子浏览量

~ 46624 小时

~ 62048 小时

1.92%

参与率

~ 3

每日帖子数

Ads index

beta

帖子存档

40 106

Repost from Kaggle Data Hub

📊 Data Science Cheat Sheets 📦 596.3 MB | 👍 5.5K | ⬇️ 73.4K 📡 @DATASETS1

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Repost from Machine Learning with Python

Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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📌 Turning 127 Million Data Points Into an Industry Report 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-31 | ⏱️ Read time: 7 min read What I learned about data wrangling, segmentation, and storytelling while building an application security report… #DataScience #AI #Python

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📌 Building a Personal AI Agent in a couple of Hours 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-31 | ⏱️ Read time: 16 min read I’ve been so surprised by how fast individual builders can now ship real and useful… #DataScience #AI #Python

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📌 Why Data Scientists Should Care About Quantum Computing 🗂 Category: AUTHOR SPOTLIGHTS 🕒 Date: 2026-03-30 | ⏱️ Read time: 6 min read Sara A. Metwalli on the rise of a promising new technology, the effects of LLM… #DataScience #AI #Python

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📌 How to Lie with Statistics with your Robot Best Friend 🗂 Category: SCIENCE 🕒 Date: 2026-03-30 | ⏱️ Read time: 12 min read What is p hacking, is it bad, and can you get ai to do it… #DataScience #AI #Python

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📌 Explainable AI in Production: A Neuro-Symbolic Model for Real-Time Fraud Detection 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-03-30 | ⏱️ Read time: 16 min read SHAP needs 30 ms to explain a fraud prediction. That explanation is stochastic, runs after… #DataScience #AI #Python

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📌 How to Become an AI Engineer Fast (Skills, Projects, Salary) 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-29 | ⏱️ Read time: 12 min read Spoiler, it will take longer than 3 months #DataScience #AI #Python

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📌 Self-Healing Neural Networks in PyTorch: Fix Model Drift in Real Time Without Retraining 🗂 Category: DEEP LEARNING 🕒 Date: 2026-03-29 | ⏱️ Read time: 22 min read What happens when your production model drifts and retraining isn’t an option? This article shows… #DataScience #AI #Python

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📌 Using OpenClaw as a Force Multiplier: What One Person Can Ship with Autonomous Agents 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-28 | ⏱️ Read time: 25 min read It’s easier than ever to 10x your output with agentic AI. #DataScience #AI #Python

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📌 From NetCDF to Insights: A Practical Pipeline for City-Level Climate Risk Analysis 🗂 Category: CLIMATE CHANGE 🕒 Date: 2026-03-28 | ⏱️ Read time: 7 min read Integrating CMIP6 projections, ERA5 reanalysis, and impact models into a lightweight, interpretable workflow #DataScience #AI #Python

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📌 How ElevenLabs Voice AI Is Replacing Screens in Warehouse and Manufacturing Operations 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-27 | ⏱️ Read time: 10 min read A warehouse picking operation is the process of collecting items from storage locations to fulfil… #DataScience #AI #Python

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📌 A Beginner’s Guide to Quantum Computing with Python 🗂 Category: QUANTUM COMPUTING 🕒 Date: 2026-03-27 | ⏱️ Read time: 7 min read Simulate a quantum computer with Qiskit #DataScience #AI #Python

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📌 Building a Production-Grade Multi-Node Training Pipeline with PyTorch DDP 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-27 | ⏱️ Read time: 14 min read A practical, code-driven guide to scaling deep learning across machines — from NCCL process groups… #DataScience #AI #Python

40 106

Repost from Machine Learning with Python

Classical filters & convolution: The heart of computer vision Before Deep Learning exploded onto the scene, traditional computer vision centered on filters. Filters were small, hand-engineered matrices that you convolved with an image to detect specific features like edges, corners, or textures. In this article, we will dive into the details of classical filters and convolution operation - how they work, why they matter, and how to implement them. More: https://www.vizuaranewsletter.com/p/classical-filters-and-convolution

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📌 What the Bits-over-Random Metric Changed in How I Think About RAG and Agents 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-26 | ⏱️ Read time: 19 min read Why retrieval that looks excellent on paper can still behave like noise in real RAG… #DataScience #AI #Python

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📌 Beyond Code Generation: AI for the Full Data Science Workflow 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-26 | ⏱️ Read time: 10 min read Using Codex and MCP to connect Google Drive, GitHub, BigQuery, and analysis in one real workflow #DataScience #AI #Python

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📌 How to Make Your AI App Faster and More Interactive with Response Streaming 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-26 | ⏱️ Read time: 8 min read In my latest posts, we’ve talked a lot about prompt caching as well as caching… #DataScience #AI #Python

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📌 My Models Failed. That’s How I Became a Better Data Scientist. 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-25 | ⏱️ Read time: 9 min read Data Leakage, Real-World Models, and the Path to Production AI in Healthcare #DataScience #AI #Python