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Machine Learning with Python
前往频道在 Telegram
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
显示更多📈 Telegram 频道 Machine Learning with Python 的分析概览
频道 Machine Learning with Python (@codeprogrammer) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 67 813 名订阅者，在 教育 类别中位列第 2 416，并在 印度 地区排名第 5 038 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 67 813 名订阅者。
根据 09 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 70，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 2.94%。内容发布后 24 小时内通常能获得 2.44% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 1 997 次浏览，首日通常累积 1 652 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 7。
- 主题关注点： 内容集中在 insidead, learning, degree, evaluation, algorithm 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：
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
凭借高频更新（最新数据采集于 10 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。
67 813
订阅者
+1024 小时
+127 天
+7030 天
帖子存档
+1
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Repost from Machine Learning
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[1] 𝗪𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗶𝗻 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀? 𝗡𝗼𝘁 𝗔𝗹𝗹 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗶𝘀 𝗡𝗲𝗲𝗱𝗲𝗱
[2] 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗦𝘁𝗿𝗶𝗻𝗴𝘀: 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 𝗳𝗼𝗿 𝗕𝗮𝘆𝗲𝘀𝗶𝗮𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻
[3] 𝗠𝗢𝗗𝗘𝗟 𝗦𝗪𝗔𝗥𝗠𝗦: 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃𝗲 𝗦𝗲𝗮𝗿𝗰𝗵 𝘁𝗼 𝗔𝗱𝗮𝗽𝘁 𝗟𝗟𝗠 𝗘𝘅𝗽𝗲𝗿𝘁𝘀 𝘃𝗶𝗮 𝗦𝘄𝗮𝗿𝗺 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲
[4] 𝗧𝗛𝗜𝗡𝗞𝗜𝗡𝗚 𝗟𝗟𝗠𝗦: 𝗚𝗲𝗻𝗲𝗿𝗮𝗹 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 𝗙𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗧𝗵𝗼𝘂𝗴𝗵𝘁 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻
[5] 𝗢𝗽𝗲𝗻𝗩𝗟𝗔: 𝗔𝗻 𝗢𝗽𝗲𝗻-𝗦𝗼𝘂𝗿𝗰𝗲 𝗩𝗶𝘀𝗶𝗼𝗻-𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲-𝗔𝗰𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹
[6] 𝗥𝗧-𝟭: 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿 𝗳𝗼𝗿 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗔𝘁 𝗦𝗰𝗮𝗹𝗲
[7] π𝟬: 𝗔 𝗩𝗶𝘀𝗶𝗼𝗻-𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲-𝗔𝗰𝘁𝗶𝗼𝗻 𝗙𝗹𝗼𝘄 𝗠𝗼𝗱𝗲𝗹 𝗳𝗼𝗿 𝗚𝗲𝗻𝗲𝗿𝗮𝗹 𝗥𝗼𝗯𝗼𝘁 𝗖𝗼𝗻𝘁𝗿𝗼𝗹
[8] 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻: 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗟𝗼𝗻𝗴-𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗟𝗟𝗠 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝘃𝗶𝗮 𝗩𝗲𝗰𝘁𝗼𝗿 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹
[9] 𝗣-𝗥𝗔𝗚: 𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀𝗶𝘃𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗙𝗼𝗿 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗼𝗻 𝗘𝗺𝗯𝗼𝗱𝗶𝗲𝗱 𝗘𝘃𝗲𝗿𝘆𝗱𝗮𝘆 𝗧𝗮𝘀𝗸
[10] 𝗥𝘂𝗔𝗚: 𝗟𝗲𝗮𝗿𝗻𝗲𝗱-𝗥𝘂𝗹𝗲-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗙𝗼𝗿 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀
[11] 𝗢𝗻 𝘁𝗵𝗲 𝗦𝘂𝗿𝗽𝗿𝗶𝘀𝗶𝗻𝗴 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗻𝗲𝘀𝘀 𝗼𝗳 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗧𝗿𝗮𝗻𝘀𝗳𝗲𝗿 𝗳𝗼𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀
[12] 𝗠𝗶𝘅𝘁𝘂𝗿𝗲-𝗼𝗳-𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀: 𝗔 𝗦𝗽𝗮𝗿𝘀𝗲 𝗮𝗻𝗱 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗠𝘂𝗹𝘁𝗶-𝗠𝗼𝗱𝗮𝗹 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀
[13]-[14] 𝗘𝗱𝗶𝗳𝘆 𝟯𝗗: 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗛𝗶𝗴𝗵-𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝟯𝗗 𝗔𝘀𝘀𝗲𝘁 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻
[15] 𝗕𝘆𝘁𝗲 𝗟𝗮𝘁𝗲𝗻𝘁 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿: 𝗣𝗮𝘁𝗰𝗵𝗲𝘀 𝗦𝗰𝗮𝗹𝗲 𝗕𝗲𝘁𝘁𝗲𝗿 𝗧𝗵𝗮𝗻 𝗧𝗼𝗸𝗲𝗻𝘀
[16]-[18] 𝗗𝗲𝗲𝗽𝗦𝗲𝗲𝗸-𝗩𝟯 (𝗣𝗮𝗿𝘁 𝟭-𝟯)
[19] 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗡𝗼𝗿𝗺𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻
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Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
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✅ https://t.me/Codeprogrammer
𝗪𝗵𝘆 𝗘𝘃𝗲𝗿𝘆 𝗔𝘀𝗽𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗦𝗵𝗼𝘂𝗹𝗱 𝗟𝗲𝗮𝗿𝗻 𝗣𝘆𝗦𝗽𝗮𝗿𝗸
If you’re working with large datasets, tools like Pandas can hit limits fast. That’s where 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 comes in—designed to scale effortlessly across big data workloads.
𝗪𝗵𝗮𝘁 𝗶𝘀 𝗣𝘆𝗦𝗽𝗮𝗿𝗸?
PySpark is the Python API for Apache Spark—a powerful engine for distributed data processing. It's widely used to build scalable ETL pipelines and handle millions of records efficiently.
𝗪𝗵𝘆 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗜𝘀 𝗮 𝗠𝘂𝘀𝘁-𝗛𝗮𝘃𝗲 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀:
✔️ Scales to handle massive datasets
✔️ Designed for distributed computing
✔️ Blends SQL with Python for flexible logic
✔️ Perfect for building end-to-end ETL pipelines
✔️ Supports integrations like Hive, Kafka, and Delta Lake
𝗤𝘂𝗶𝗰𝗸 𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("Example").getOrCreate()
df = spark.read.csv("data.csv", header=True, inferSchema=True)
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