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Machine Learning

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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📈 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 40 291 名订阅者,在 技术与应用 类别中位列第 3 341,并在 叙利亚 地区排名第 226

📊 受众指标与增长动态

невідомо 创建以来,项目保持高速增长,吸引了 40 291 名订阅者。

根据 08 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 353,过去 24 小时变化为 12,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 2.31%。内容发布后 24 小时内通常能获得 1.90% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 929 次浏览,首日通常累积 766 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 09 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。

40 291
订阅者
+1224 小时
+867
+35330
帖子存档
📌 Preparing PDFs for RAGs 🗂 Category: DATA SCIENCE 🕒 Date: 2025-01-17 | ⏱️ Read time: 5 min read I created a graph storage
📌 Preparing PDFs for RAGs 🗂 Category: DATA SCIENCE 🕒 Date: 2025-01-17 | ⏱️ Read time: 5 min read I created a graph storage from dozens of annual reports (with tables)

📌 A Practical Exploration of Sora – Intuitively and Exhaustively Explained 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 202
📌 A Practical Exploration of Sora – Intuitively and Exhaustively Explained 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-01-17 | ⏱️ Read time: 23 min read A new cutting edge video generation tool, and the theory behind it

📌 Where to Start when Data is Limited: A Guide 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-01-17 | ⏱️ Read time: 23 m
📌 Where to Start when Data is Limited: A Guide 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-01-17 | ⏱️ Read time: 23 min read Overcome small data constraints & ambitious performance requirements-leveraging modern ML to surpass conventional methods.

📌 My Experience Switching From Power BI to Looker (as a Senior Data Analyst) 🗂 Category: MICROSOFT 🕒 Date: 2025-01-17 | ⏱️
📌 My Experience Switching From Power BI to Looker (as a Senior Data Analyst) 🗂 Category: MICROSOFT 🕒 Date: 2025-01-17 | ⏱️ Read time: 17 min read What you need to know before you switch from Power BI to Looker.

📌 Showcasing Soaring Wildfire Counts With Streamlit and Python: A Powerful Approach 🗂 Category: DATA VISUALIZATION 🕒 Date:
📌 Showcasing Soaring Wildfire Counts With Streamlit and Python: A Powerful Approach 🗂 Category: DATA VISUALIZATION 🕒 Date: 2025-01-18 | ⏱️ Read time: 13 min read Analyzing historical wildfire trends in Canada with public data

📌 Modern Data And Application Engineering Breaks the Loss of Business Context 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-01
📌 Modern Data And Application Engineering Breaks the Loss of Business Context 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-01-18 | ⏱️ Read time: 16 min read Here’s how your data retains its business relevance as it travels through your enterprise

📌 How to Log Your Data with MLflow 🗂 Category: DATA SCIENCE 🕒 Date: 2025-01-19 | ⏱️ Read time: 12 min read Mastering data
📌 How to Log Your Data with MLflow 🗂 Category: DATA SCIENCE 🕒 Date: 2025-01-19 | ⏱️ Read time: 12 min read Mastering data logging in MLOps for your AI workflow

📌 Zero-Shot Player Tracking in Tennis with Kalman Filtering 🗂 Category: 🕒 Date: 2025-01-19 | ⏱️ Read time: 10 min read Aut
📌 Zero-Shot Player Tracking in Tennis with Kalman Filtering 🗂 Category: 🕒 Date: 2025-01-19 | ⏱️ Read time: 10 min read Automated tennis tracking without labels: GroundingDINO, Kalman filtering, and court homography.

📌 The Concepts Data Professionals Should Know in 2025: Part 1 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-01-19 | ⏱️ Read ti
📌 The Concepts Data Professionals Should Know in 2025: Part 1 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-01-19 | ⏱️ Read time: 14 min read From Data Lakehouses to Event-Driven Architecture – Master 12 data concepts and turn them into…

📌 Designing, Building & Deploying an AI Chat App from Scratch (Part 1) 🗂 Category: 🕒 Date: 2025-01-20 | ⏱️ Read time: 19 m
📌 Designing, Building & Deploying an AI Chat App from Scratch (Part 1) 🗂 Category: 🕒 Date: 2025-01-20 | ⏱️ Read time: 19 min read Microservices Architecture and Local Development

📌 Designing, Building & Deploying an AI Chat App from Scratch (Part 2) 🗂 Category: 🕒 Date: 2025-01-20 | ⏱️ Read time: 20 m
📌 Designing, Building & Deploying an AI Chat App from Scratch (Part 2) 🗂 Category: 🕒 Date: 2025-01-20 | ⏱️ Read time: 20 min read Cloud Deployment and Scaling

📌 The Concepts Data Professionals Should Know in 2025: Part 2 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-01-20 | ⏱️
📌 The Concepts Data Professionals Should Know in 2025: Part 2 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-01-20 | ⏱️ Read time: 14 min read From AI Agent to Human-In-The-Loop – Master 12 critical data concepts and turn them into…

📌 Neural Networks for Time-Series Imputation: Tackling Missing Data 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-01-22 | ⏱️ R
📌 Neural Networks for Time-Series Imputation: Tackling Missing Data 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-01-22 | ⏱️ Read time: 11 min read Part 3: Discover how a simple Keras sequential model can be effective

📌 Human Minds and Machine Learning Models 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-01-22 | ⏱️ Read time: 14 min read Expl
📌 Human Minds and Machine Learning Models 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-01-22 | ⏱️ Read time: 14 min read Exploring the parallels and differences between psychology and machine learning

📌 How to Utilize ModernBERT and Synthetic Data for Robust Text Classification 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-01
📌 How to Utilize ModernBERT and Synthetic Data for Robust Text Classification 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-01-22 | ⏱️ Read time: 10 min read Learn how to fine-tune ModernBERT and create augmentations of text samples

📌 How to Evaluate LLM Summarization 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-01-22 | ⏱️ Read time: 18 min read A p
📌 How to Evaluate LLM Summarization 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-01-22 | ⏱️ Read time: 18 min read A practical and effective guide for evaluating AI summaries

📌 Topic Modelling in Business Intelligence: FASTopic and BERTopic in Code 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-01-22
📌 Topic Modelling in Business Intelligence: FASTopic and BERTopic in Code 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-01-22 | ⏱️ Read time: 11 min read A comparison of two cutting-edge dynamic topic models solving consumer complaints classification exercise

📌 Understanding Emergent Capabilities in LLMs: Lessons from Biological Systems 🗂 Category: 🕒 Date: 2025-01-22 | ⏱️ Read ti
📌 Understanding Emergent Capabilities in LLMs: Lessons from Biological Systems 🗂 Category: 🕒 Date: 2025-01-22 | ⏱️ Read time: 24 min read How natural systems fundamental laws help explain AI’s unexpected abilities

📌 Harmonizing and Pooling Datasets for Health Research in R 🗂 Category: CODING 🕒 Date: 2025-01-22 | ⏱️ Read time: 11 min r
📌 Harmonizing and Pooling Datasets for Health Research in R 🗂 Category: CODING 🕒 Date: 2025-01-22 | ⏱️ Read time: 11 min read R code to extract data from unique datasets and combine them in one harmonized dataset…

📌 Behind the Scenes of a Successful Data Analytics Project 🗂 Category: DATA SCIENCE 🕒 Date: 2025-01-23 | ⏱️ Read time: 10
📌 Behind the Scenes of a Successful Data Analytics Project 🗂 Category: DATA SCIENCE 🕒 Date: 2025-01-23 | ⏱️ Read time: 10 min read Learn the steps to approach any data analytics project like a pro.