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
📌 LLMs, AI Agents, the Economics of Generative AI, and Other August Must-Reads
🗂 Category: DATA SCIENCE
🕒 Date: 2024-08-29 | ⏱️ Read time: 5 min read
The stories that resonated the most with our community in the past month
40 202
📌 The Essential Guide to Error-Checking and Reviewing Presentations
🗂 Category: DATA SCIENCE
🕒 Date: 2024-08-29 | ⏱️ Read time: 7 min read
An overlooked skill for Data Scientists (and not only)
40 202
📌 How to Create Custom Color Palettes in Matplotlib – Discrete vs. Linear Colormaps, Explained
🗂 Category: DATA SCIENCE
🕒 Date: 2024-08-29 | ⏱️ Read time: 6 min read
Actionable guide on how to bring custom colors to personalize your charts
40 202
📌 The Smarter Way of Using AI in Programming
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2024-08-29 | ⏱️ Read time: 7 min read
avoid the outdated methods of integrating AI into your coding workflow by going beyond ChatGPT
40 202
📌 Stop Manually Sorting Your List In Python If Performance Is Concerned
🗂 Category: DATA SCIENCE
🕒 Date: 2024-08-29 | ⏱️ Read time: 8 min read
A sorted collection library that is as fast as C-extensions
40 202
📌 Stop Being Data-Driven
🗂 Category: DATA SCIENCE
🕒 Date: 2024-08-30 | ⏱️ Read time: 12 min read
Why we are fooled by data and how to stop it
40 202
📌 How to Build a Genetic Algorithm from Scratch in Python
🗂 Category: DATA SCIENCE
🕒 Date: 2024-08-30 | ⏱️ Read time: 16 min read
A complete walkthrough on how one can build a Genetic Algorithm from scratch in Python,…
40 202
📌 Causal Machine Learning for Customer Retention: a Practical Guide with Python
🗂 Category: MACHINE LEARNING
🕒 Date: 2024-08-30 | ⏱️ Read time: 25 min read
An accessible guide to leveraging causal machine learning for optimizing client retention strategies
40 202
📌 How to Build a Powerful Deep Research System
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-10-04 | ⏱️ Read time: 6 min read
Learn how to access vasts amounts of information with your own deep research system
40 202
📌 Real-Time Intelligence in Microsoft Fabric: The Ultimate Guide
🗂 Category: DATA SCIENCE
🕒 Date: 2025-10-04 | ⏱️ Read time: 21 min read
Once upon a time, handling streaming data was considered an avant-garde approach. Since the introduction of relational…
40 202
📌 The Power of Pandas Plots: Backends
🗂 Category: DATA SCIENCE
🕒 Date: 2024-08-30 | ⏱️ Read time: 6 min read
Create interactive graphics from Pandas effortlessly
40 202
📌 Hands On Neural Networks and Time Series, with Python
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2024-08-30 | ⏱️ Read time: 14 min read
From the very simple Feed Forward Neural Networks to the majestic transformers: everything you need…
40 202
📌 A Comprehensive Introduction to Marketing Data Engineering
🗂 Category: DATA ENGINEERING
🕒 Date: 2024-08-30 | ⏱️ Read time: 21 min read
Fundamentals, responsibilities, and challenges
40 202
📌 Compressing Large Language Models (LLMs)
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2024-08-30 | ⏱️ Read time: 12 min read
Make LLMs 10X smaller without sacrificing performance
40 202
Awesome interactive textbook on probability theory and statistics
Inside are clear visualizations, interactive elements, and minimal dry theory. You can tweak distributions, sample datasets, play with confidence intervals, and clearly see how it all works
Get it here, I recommend opening it on a desktop
https://seeing-theory.brown.edu/
👉 @DataScienceM
40 202
📌 ChatGPT vs. Claude vs. Gemini for Data Analysis (Part 3): Best AI Assistant for Machine Learning
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2024-08-30 | ⏱️ Read time: 12 min read
How AI can accelerate your ML projects from feature engineering to model training
40 202
📌 Decision Tree Classifier, Explained: A Visual Guide with Code Examples for Beginners
🗂 Category: MACHINE LEARNING
🕒 Date: 2024-08-30 | ⏱️ Read time: 11 min read
A fresh look on our favorite upside-down tree
40 202
📌 Navigating the New Types of LLM Agents and Architectures
🗂 Category:
🕒 Date: 2024-08-30 | ⏱️ Read time: 12 min read
My thanks to John Gilhuly for his contributions to this piece If 2023 was the…
40 202
📌 Targeting variants for maximum impact
🗂 Category: MACHINE LEARNING
🕒 Date: 2024-08-30 | ⏱️ Read time: 8 min read
How to use causal inference to improve key business metrics Egor Kraev and Alexander Polyakov…
40 202
📌 The Ultimate Guide to Vision Transformers
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2024-08-30 | ⏱️ Read time: 8 min read
A comprehensive guide to the Vision Transformer (ViT) that revolutionized computer vision.
现已上线!2025 年 Telegram 研究 — 年度关键洞察 
