现已上线！2025 年 Telegram 研究 — 年度关键洞察 
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 40 202 名订阅者，在 技术与应用 类别中位列第 3 365，并在 叙利亚 地区排名第 227 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 40 202 名订阅者。
根据 02 七月, 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 343，过去 24 小时变化为 10，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 1.99%。内容发布后 24 小时内通常能获得 2.28% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 800 次浏览，首日通常累积 915 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 03 七月, 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。
40 202
订阅者
+1024 小时
+837 天
+34330 天
帖子存档
40 202
📌 Essential Guide to Continuous Ranked Probability Score (CRPS) for Forecasting
🗂 Category: DATA SCIENCE
🕒 Date: 2024-08-31 | ⏱️ Read time: 7 min read
Learn how to evaluate probabilistic forecasts and how CRPS relates to other metrics
40 202
📌 How to Deal with Time Series Outliers
🗂 Category: DATA SCIENCE
🕒 Date: 2024-08-31 | ⏱️ Read time: 6 min read
Understanding, detecting and replacing outliers in time series
40 202
📌 Data Scientists Can’t Excel in Python Without Mastering These Functions
🗂 Category: DATA SCIENCE
🕒 Date: 2024-08-31 | ⏱️ Read time: 11 min read
Introduction of Python’s core functions, use cases, scripts, and underlying mechanisms
40 202
📌 Streamline Property Data Management: Advanced Data Extraction & Retrieval with Indexify
🗂 Category:
🕒 Date: 2024-08-31 | ⏱️ Read time: 15 min read
A Step-by-Step Guide to Document Querying with Indexify
40 202
📌 The DIY Path to AI Product Management: Picking a Starter Project
🗂 Category: CHATGPT
🕒 Date: 2024-08-31 | ⏱️ Read time: 8 min read
Building real-world skills through hands-on trial and error.
40 202
📌 Building Scalable Data Platforms
🗂 Category: ANALYTICS
🕒 Date: 2024-09-01 | ⏱️ Read time: 14 min read
Data Mesh trends in data platform design
40 202
📌 Training AI Models on CPU
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2024-09-01 | ⏱️ Read time: 16 min read
Revisiting CPU for ML in an Era of GPU Scarcity
40 202
📌 Create Your Own Meal Planner Using ChatGPT
🗂 Category: CHATGPT
🕒 Date: 2024-09-02 | ⏱️ Read time: 19 min read
A brief guide to prompt engineering
40 202
📌 Mathematics of Love: Optimizing a Dining-Room Seating Arrangement for Weddings with Python
🗂 Category: DATA SCIENCE
🕒 Date: 2024-09-02 | ⏱️ Read time: 19 min read
Solving the Restricted Quadratic Multi-Knapsack Problem (RQMKP) with mathematical programming and Python
40 202
📌 An Easy Way to Remove Tourists from Photos
🗂 Category: PYTHON
🕒 Date: 2024-09-02 | ⏱️ Read time: 9 min read
Image cleanup with Python, PIL, and OpenCV
40 202
📌 Encoding Categorical Data, Explained: A Visual Guide with Code Example for Beginners
🗂 Category: DATA SCIENCE
🕒 Date: 2024-09-02 | ⏱️ Read time: 10 min read
Six ways of matchmaking categories and numbers
40 202
📌 Use R to build Clinical Flowchart with shinyCyJS
🗂 Category:
🕒 Date: 2024-09-03 | ⏱️ Read time: 6 min read
Customizable R package for Graph / Network visualization
40 202
📌 Subway Route Data Extraction with Overpass API: A Step-by-Step Guide
🗂 Category: DATA SCIENCE
🕒 Date: 2024-09-03 | ⏱️ Read time: 11 min read
Simplify Geodata Extraction from OpenStreetMaps via the Overpass API
40 202
📌 Information in Noise
🗂 Category: DATA SCIENCE
🕒 Date: 2024-09-03 | ⏱️ Read time: 4 min read
Two Techniques for Visualizing Many Time-Series at Once
40 202
📌 5 Pillars for a Hyper-Optimized AI Workflow
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2024-09-03 | ⏱️ Read time: 8 min read
A gentle introduction to a methodology for creating production-ready, extensible & highly optimized AI workflows
40 202
📌 Line-By-Line, Let’s Reproduce GPT-2: Section 3 – Training
🗂 Category:
🕒 Date: 2024-09-03 | ⏱️ Read time: 20 min read
This blog post will go line-by-line through the code in Section 3 of Andrej Karpathy’s…
40 202
📌 Using Generative AI To Get Insights From Disorderly Data
🗂 Category:
🕒 Date: 2024-09-03 | ⏱️ Read time: 41 min read
Best practices for using Large Language Models to extract actionable insights even with poor metadata
40 202
📌 Here Comes Mamba: The Selective State Space Model
🗂 Category: DEEP LEARNING
🕒 Date: 2024-09-03 | ⏱️ Read time: 22 min read
Part 3 – Towards Mamba State Space Models for Images, Videos and Time Series
40 202
📌 Diving Deeper with Structured Outputs
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2024-09-03 | ⏱️ Read time: 10 min read
Enhancing our understanding and optimal usage of structured outputs
40 202
📌 Approximating Stochastic Functions with Multivariate Outputs
🗂 Category:
🕒 Date: 2024-09-04 | ⏱️ Read time: 25 min read
A generic approach for training probabilistic machine learning models
