现已上线！2025 年 Telegram 研究 — 年度关键洞察 
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 67 812 名订阅者，在 教育 类别中位列第 2 404，并在 印度 地区排名第 5 049 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 67 812 名订阅者。
根据 05 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 77，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 2.60%。内容发布后 24 小时内通常能获得 2.50% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 1 767 次浏览，首日通常累积 1 695 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 6。
- 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 07 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。
67 812
订阅者
+924 小时
+587 天
+7730 天
帖子存档
nature papers: 1400$
Q1 and Q2 papers 900$
Q3 and Q4 papers 500$
Doctoral thesis (complete) 700$
M.S thesis 300$
paper simulation 200$
Contact me
https://t.me/m/-nTmpj5vYzNk
200$ to 20k$ SOL Challenge!
As promised, i will do another challenge for those who missed the previous one!
Last one we completed in 6 days, let’s do this one even quicker!
Join my free group Before closing 👇
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Python Object-Oriented Programming - 5e 2025
Available here free
https://signal.group/#CjQKIPcpEqLQow53AG7RHjeVk-4sc1TFxyym3r0gQQzV-OPpEhCPw_-kRmJ8LlC13l0WiEfp
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
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/Codeprogrammer
Repost from Python Courses & Resources
Learn how LLMs work in less than 10 minutes
And honestly? This is probably the best visualization of #LLMs ever made.
https://t.me/Python53
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Awesome open-source project to learn more about Generative Adversarial Networks.
We found this interactive website that shows you visually how #GANs work.
GAN Lab Website: https://lnkd.in/eYV8QvrJ
https://t.me/CodeProgrammer 🩷
For those interested in joining our Premium channel, which contains over 5,000 books and 200 courses in machine learning, programming languages, data analysis, LLM, and other related fields with daily updates>
We offer a 15-minute free trial to explore the channel's content. Afterward, you can subscribe to the channel for $50 – a permanent subscription.
Contact me:
t.me/HusseinSheikho
All assignments for the #Stanford The Modern Software Developer course are now available online.
This is the first full-fledged university course that covers how code-generative #LLMs are changing every stage of the development lifecycle. The assignments are designed to take you from a beginner to a confident expert in using AI to boost productivity in development.
Enjoy your studies! ✌️
(https://github.com/mihail911/modern-software-dev-assignments
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How to test code without a real database
During unit testing, connecting to a real DB is unnecessary: • tests run slowly • become unstable • require a working serverIt is much better to mock the call to
pandas.read_sql and return dummy data
Example function:
def query_user_data(user_id):
query = f"SELECT id, name FROM users WHERE id = {user_id}"
return pd.read_sql(query, "postgresql://localhost/mydb")from unittest.mock import patch
import pandas as pd
@patch("pandas.read_sql")
def test_database_query_mocked(mock_read_sql):
mock_read_sql.return_value = pd.DataFrame(
{"id": [123], "name": ["Alice"]}
)
result = query_user_data(user_id=123)
assert result["name"].iloc[0] == "Alice"
200$ to 20k$ SOL Challenge!
As promised, i will do another challenge for those who missed the previous one!
Last one we completed in 6 days, let’s do this one even quicker!
Join my free group Before closing 👇
https://t.me/+DAKLP7eUy9Y3ZjY0
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