现已上线！2025 年 Telegram 研究 — 年度关键洞察 
Machine Learning & Artificial Intelligence | Data Science Free Courses
前往频道在 Telegram
Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence Admin: @coderfun
显示更多📈 Telegram 频道 Machine Learning & Artificial Intelligence | Data Science Free Courses 的分析概览
频道 Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 66 676 名订阅者，在 教育 类别中位列第 2 463，并在 马来西亚 地区排名第 433 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 66 676 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 518，过去 24 小时变化为 -9，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 0.86%。内容发布后 24 小时内通常能获得 0.79% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 572 次浏览，首日通常累积 524 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 4。
- 主题关注点： 内容集中在 sellerflash, waybienad, pricing, buybox, buyer 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：
“Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence
Admin: @coderfun”
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 教育 类别中的关键影响点。
66 676
订阅者
-924 小时
+77 天
+51830 天
帖子存档
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✅ Data Science Mistakes Beginners Should Avoid ⚠️📉
1️⃣ Skipping the Basics
• Jumping into ML without Python, Stats, or Pandas
✅ Build strong foundations in math, programming & EDA first
2️⃣ Not Understanding the Problem
• Applying models blindly
• Irrelevant features and metrics
✅ Always clarify business goals before coding
3️⃣ Treating Data Cleaning as Optional
• Training on dirty/incomplete data
✅ Spend time on preprocessing — it’s 70% of real work
4️⃣ Using Complex Models Too Early
• Overfitting small datasets
• Ignoring simpler, interpretable models
✅ Start with baseline models (Logistic Regression, Decision Trees)
5️⃣ No Evaluation Strategy
• Relying only on accuracy
✅ Use proper metrics (F1, AUC, MAE) based on problem type
6️⃣ Not Visualizing Data
• Missed outliers and patterns
✅ Use Seaborn, Matplotlib, Plotly for EDA
7️⃣ Poor Feature Engineering
• Feeding raw data into models
✅ Create meaningful features that boost performance
8️⃣ Ignoring Domain Knowledge
• Features don’t align with real-world logic
✅ Talk to stakeholders or do research before modeling
9️⃣ No Practice with Real Datasets
• Kaggle-only learning
✅ Work with messy, real-world data (open data portals, APIs)
🔟 Not Documenting or Sharing Work
• No GitHub, no portfolio
✅ Document notebooks, write blogs, push projects online
💬 Tap ❤️ for more!
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