Machine Learning & Artificial Intelligence | Data Science Free Courses
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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
数据加载中...
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吸引订阅者
六月 '26
六月 '26
+638
在7个频道中
五月 '26
+1 246
在1个频道中
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四月 '26
+953
在6个频道中
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三月 '26
+599
在1个频道中
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二月 '26
+1 067
在6个频道中
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一月 '26
+1 274
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十二月 '25
+1 119
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十一月 '25
+1 238
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十月 '25
+770
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九月 '25
+388
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八月 '25
+702
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七月 '25
+770
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六月 '25
+1 182
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五月 '25
+2 796
在31个频道中
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四月 '25
+4 069
在33个频道中
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三月 '25
+1 209
在15个频道中
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二月 '25
+583
在12个频道中
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一月 '25
+654
在26个频道中
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十二月 '24
+808
在4个频道中
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十一月 '24
+2 333
在9个频道中
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十月 '24
+2 568
在6个频道中
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九月 '24
+3 223
在19个频道中
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八月 '24
+4 069
在12个频道中
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七月 '24
+5 963
在22个频道中
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六月 '24
+5 428
在7个频道中
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+4 646
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四月 '24
+4 389
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三月 '24
+4 461
在5个频道中
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二月 '24
+3 244
在0个频道中
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一月 '24
+5 260
在6个频道中
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十二月 '23
+3 737
在0个频道中
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十一月 '23
+370
在4个频道中
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十月 '23
+302
在3个频道中
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九月 '23
+528
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八月 '23
+900
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七月 '23
+846
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六月 '23
+21
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五月 '23
+27
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四月 '23
+28
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三月 '23
+19
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二月 '23
+24
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一月 '23
+30
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十二月 '22
+44
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十一月 '22
+123
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十月 '22
+68
在0个频道中
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九月 '22
+121
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八月 '22
+42
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七月 '22
+75
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六月 '22
+40
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五月 '22
+224
在0个频道中
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四月 '22
+182
在0个频道中
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三月 '22
+94
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二月 '22
+285
在0个频道中
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一月 '22
+42
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十二月 '21
+11
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十一月 '21
+11
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十月 '21
+174
在0个频道中
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九月 '21
+398
在0个频道中
| 日期 | 订阅者增长 | 提及 | 频道 | |
| 23 六月 | +37 | |||
| 22 六月 | +11 | |||
| 21 六月 | +16 | |||
| 20 六月 | +17 | |||
| 19 六月 | +10 | |||
| 18 六月 | +22 | |||
| 17 六月 | +9 | |||
| 16 六月 | +26 | |||
| 15 六月 | +34 | |||
| 14 六月 | +55 | |||
| 13 六月 | +52 | |||
| 12 六月 | +34 | |||
| 11 六月 | +20 | |||
| 10 六月 | +28 | |||
| 09 六月 | +25 | |||
| 08 六月 | +23 | |||
| 07 六月 | +29 | |||
| 06 六月 | +25 | |||
| 05 六月 | +26 | |||
| 04 六月 | +36 | |||
| 03 六月 | +37 | |||
| 02 六月 | +37 | |||
| 01 六月 | +29 |
频道帖子
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| 14 | ✅ 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
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现已上线!2025 年 Telegram 研究 — 年度关键洞察 
