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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 40 100 名订阅者,在 技术与应用 类别中位列第 3 398,并在 叙利亚 地区排名第 232

📊 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 40 100 名订阅者。

根据 23 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 379,过去 24 小时变化为 30,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 1.92%。内容发布后 24 小时内通常能获得 1.16% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 770 次浏览,首日通常累积 466 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 24 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 技术与应用 类别中的关键影响点。

40 100
订阅者
+3024 小时
+337
+37930
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六月 '26
六月 '26
+488
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+748
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+423
在8个频道中
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三月 '26
+286
在7个频道中
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二月 '26
+467
在12个频道中
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一月 '26
+624
在13个频道中
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十二月 '25
+678
在14个频道中
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十一月 '25
+642
在3个频道中
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十月 '25
+1 052
在9个频道中
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九月 '25
+1 012
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八月 '25
+470
在3个频道中
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七月 '25
+1 113
在14个频道中
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六月 '25
+876
在5个频道中
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五月 '25
+1 030
在2个频道中
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四月 '25
+1 368
在2个频道中
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三月 '25
+820
在1个频道中
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二月 '25
+1 068
在6个频道中
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一月 '25
+1 062
在4个频道中
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十二月 '24
+895
在4个频道中
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十一月 '24
+1 359
在14个频道中
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十月 '24
+1 841
在14个频道中
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九月 '24
+1 406
在16个频道中
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八月 '24
+1 739
在13个频道中
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七月 '24
+1 108
在14个频道中
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六月 '24
+1 335
在12个频道中
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五月 '24
+1 257
在15个频道中
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四月 '24
+1 349
在7个频道中
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三月 '24
+1 778
在11个频道中
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二月 '24
+1 882
在12个频道中
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一月 '24
+2 482
在16个频道中
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十二月 '23
+1 638
在17个频道中
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十一月 '23
+1 420
在14个频道中
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十月 '23
+811
在15个频道中
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九月 '23
+1 076
在0个频道中
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八月 '23
+1 246
在0个频道中
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七月 '23
+4 094
在0个频道中
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六月 '23
+1 831
在0个频道中
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五月 '23
+1 304
在0个频道中
日期
订阅者增长
提及
频道
25 六月+1
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07 六月+18
06 六月+17
05 六月+24
04 六月+24
03 六月+26
02 六月+33
01 六月+31
频道帖子
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PANDAS — CHEAT SHEET 1. DATA LOADING Method | What it does ----------------+-------------------- pd.read_csv() | Reads CSV file pd.read_excel() | Reads Excel file pd.read_sql() | Reads data from SQL pd.read_json() | Reads JSON file 2. DATA ANALYSIS Method | What it does --------------+--------------------------- df.head() | Shows first rows df.info() | Table information df.describe() | Statistics by columns df.shape | Table size (rows, columns) df.columns | List of column names 3. DATA SELECTION Method | What it does -----------+---------------------------------- df.loc[] | Selection by row and column names df.iloc[] | Selection by indices df.query() | Filtering by condition 4. DATA CLEANING Method | What it does ---------------------+-------------------------------- df.isnull() | Check for missing values (NULL) df.dropna() | Remove rows with missing values df.fillna() | Fill missing values df.drop_duplicates() | Remove duplicates df.astype() | Change data type 5. ANALYTICS Method | What it does ------------------+---------------------------- df.groupby() | Data grouping df.agg() | Aggregation in groups df.value_counts() | Count of unique values df.mean() | Mean value df.median() | Median df.corr() | Correlation between columns 6. DATA MERGING Method | What it does ------------+--------------------- pd.merge() | SQL JOIN by column pd.join() | JOIN by index pd.concat() | Glue tables together ⭐ TOP 10 METHODS read_csv() head() info() loc[] iloc[] query() groupby() merge() fillna() sort_values()
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PANDAS — CHEAT SHEET 1. DATA LOADING Method          | What it does       ----------------+-------------------- pd.read_csv()   | Reads CSV file     pd.read_excel() | Reads Excel file   pd.read_sql()   | Reads data from SQL pd.read_json()  | Reads JSON file    2. DATA ANALYSIS Method        | What it does              --------------+--------------------------- df.head()     | Shows first rows          df.info()     | Table information         df.describe() | Statistics by columns     df.shape      | Table size (rows, columns) df.columns    | List of column names      3. DATA SELECTION Method     | What it does                     -----------+---------------------------------- df.loc[]   | Selection by row and column names df.iloc[]  | Selection by indices             df.query() | Filtering by condition           4. DATA CLEANING Method               | What it does                   ---------------------+-------------------------------- df.isnull()          | Check for missing values (NULL) df.dropna()          | Remove rows with missing values df.fillna()          | Fill missing values            df.drop_duplicates() | Remove duplicates              df.astype()          | Change data type               5. ANALYTICS Method            | What it does               ------------------+---------------------------- df.groupby()      | Data grouping              df.agg()          | Aggregation in groups      df.value_counts() | Count of unique values     df.mean()         | Mean value                 df.median()       | Median                     df.corr()         | Correlation between columns 6. DATA MERGING Method      | What it does        ------------+--------------------- pd.merge()  | SQL JOIN by column  pd.join()   | JOIN by index       pd.concat() | Glue tables together ⭐ TOP 10 METHODS read_csv() head() info() loc[] iloc[] query() groupby() merge() fillna() sort_values()
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My favorite way to work with multiple filters in pandas.Series — not a chain of .loc, but a single mask. 🐼 The chain looks neat, but breaks on real data and easily gives unexpected results: s = pd.Series([10, 15, 20, 25, 30]) s.loc[s > 20].loc[s % 2 == 1] The problem is that the second .loc again looks at the original s, not the already filtered result. The logic gets messy. 🤯 It's more reliable to gather everything into one expression: s = pd.Series([10, 15, 20, 25, 30]) mask = (s > 20) & (s % 2 == 1) result = s.loc[mask] One mask, one point of truth. ✅ It's easier to debug. Fewer surprises when the code grows. 🚀 #Pandas #Python #DataScience #CodingTips #DataEngineering #Debugging ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A 🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more. ✅ 13 courses live + 40+ coming soon 🎯 One access, lifetime updates 🔑 Use code: PRESALE-BOOK-WAVE-2GFG 👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
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A free MIT guide to key computer vision concepts 📘 Link: https://visionbook.mit.edu/ 🔗 #ComputerVision #MIT #AI #MachineLea
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Learn AI for free directly from top companies. 🚀 1 - Anthropic: anthropic.skilljar.com 2 - Google: grow.google/ai 3 - Meta: ai.meta.com/resources/ 4 - NVIDIA: developer.nvidia.com/cuda 5 - Microsoft: learn.microsoft.com/en-us/training/ 6 - OpenAI: academy.openai.com 7 - IBM: skillsbuild.org 8 - AWS: skillbuilder.aws 9 - DeepLearning.AI: deeplearning.ai 10 - Hugging Face: huggingface.co/learn 💬 Comment "Learning" if you find this helpful. 🔄 Repost so others can take help. 🔖 Must bookmark for future reference. #AI #MachineLearning #Tech #FreeLearning #DataScience #AIForAll https://t.me/CodeProgrammer
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