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 67 821 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 67 821 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。
67 821
订阅者
+924 小时
+587 天
+7730 天
帖子存档
This GitHub repository is not a dump of tutorials.
Inside, there are 28 production-ready AI projects that can be used.
What's there:
Machine learning projects
→ Airbnb price forecasting
→ Air ticket cost calculator
→ Student performance tracker
AI for medicine
→ Chest disease detection
→ Heart disease prediction
→ Diabetes risk analysis
Generative AI applications
→ Live chatbot on Gemini
→ Medical assistant tool
→ Document analysis tool
Computer vision projects
→ Hand tracking system
→ Drug recognition app
→ OpenCV implementations
Data analysis dashboards
→ E-commerce analytics
→ Restaurant analytics
→ Cricket statistics tracker
And 10 more advanced projects coming soon:
→ Deepfake detection
→ Brain tumor classification
→ Driver drowsiness alert system
This is not just a collection of code files.
These are end-to-end working applications.
View the repository 😲
https://github.com/KalyanM45/AI-Project-Gallery
👉 @codeprogrammer
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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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Repost from Learn Python Coding
Advice on clean code in Python
Don't use "naive"
datetime without time zones. Store and process time in UTC, and display it to the user in his local time zone
import datetime
from zoneinfo import ZoneInfo
# BAD
now = datetime.datetime.now()
print(now.isoformat())
# 2025-10-21T15:03:07.332217
# GOOD
now = datetime.datetime.now(tz=ZoneInfo("UTC"))
print(now.isoformat())
# 2025-10-21T12:04:22.573590+00:00
print(now.astimezone().isoformat())
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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.me/CodeProgrammer
🔖 Machine Learning
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In scientific work, the most time is spent on reading articles, data, and reports.
On GitHub, there is a collection called Awesome AI for Science -»»» a catalog of AI tools for all stages of research.
Inside:
-» working with literature
-» data analysis
-» turning articles into posters
-» automating experiments
-» tools for biology, chemistry, physics, and other fields
GitHub: http://github.com/ai-boost/awesome-ai-for-science
The list includes Paper2Poster, MinerU, The AI Scientist, as well as articles, datasets, and frameworks.
In fact, this is a complete set of tools for AI support in scientific research.
👉 https://t.me/CodeProgrammer
Repost from Learn Python Coding
Automatic translator in Python!
We translate a text in a few lines using
deep-translator. It supports dozens of languages: from English and Russian to Japanese and Arabic.
Install the library:
pip install deep-translator
Example of use:
from deep_translator import GoogleTranslator
text = "Hello, how are you?"
result = GoogleTranslator(source="ru", target="en").translate(text)
print("Original:", text)
print("Translation:", result)
Mass translation of a list:
texts = ["Hello", "What's your name?", "See you later"]
for t in texts:
print("→", GoogleTranslator(source="ru", target="es").translate(t))
🔥 We get a mini-Google Translate right in Python: you can embed it in a chatbot, use it in notes, or automate work with the API.
🚪 @DataScience4Repost from Machine Learning with Python
🚀Stanford just completed a must-watch for anyone serious about AI:
🎓 “𝗖𝗠𝗘 𝟮𝟵𝟱: 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 & 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀” is now live entirely on YouTube and it’s pure gold.
If you’re building your AI career, stop scrolling.
This isn’t another surface-level overview. It’s the clearest, most structured intro to LLMs you could follow, straight from the Stanford Autumn 2025 curriculum.
📚 𝗧𝗼𝗽𝗶𝗰𝘀 𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝗶𝗻𝗰𝗹𝘂𝗱𝗲:
• How Transformers actually work (tokenization, attention, embeddings)
• Decoding strategies & MoEs
• LLM finetuning (LoRA, RLHF, supervised)
• Evaluation techniques (LLM-as-a-judge)
• Optimization tricks (RoPE, quantization, approximations)
• Reasoning & scaling
• Agentic workflows (RAG, tool calling)
🧠 My workflow: I usually take the transcripts, feed them into NotebookLM, and once I’ve done the lectures, I replay them during walks or commutes. That combo works wonders for retention.
🎥 Watch these now:
- Lecture 1: https://lnkd.in/dDER-qyp
- Lecture 2: https://lnkd.in/dk-tGUDm
- Lecture 3: https://lnkd.in/drAPdjJY
- Lecture 4: https://lnkd.in/e_RSgMz7
- Lecture 5: https://lnkd.in/eivMA9pe
- Lecture 6: https://lnkd.in/eYwwwMXn
- Lecture 7: https://lnkd.in/eKwkEDXV
- Lecture 8: https://lnkd.in/eEWvyfyK
- Lecture 9: https://lnkd.in/euiKRGaQ
🗓 Do yourself a favor for this 2026: block 2-3 hours per week / llectue and go through them.
If you’re in AI — whether building infra, agents, or apps — this is the foundational course you don’t want to miss.
Let’s level up.
https://t.me/CodeProgrammer 😅
🚀Stanford just completed a must-watch for anyone serious about AI:
🎓 “𝗖𝗠𝗘 𝟮𝟵𝟱: 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 & 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀” is now live entirely on YouTube and it’s pure gold.
If you’re building your AI career, stop scrolling.
This isn’t another surface-level overview. It’s the clearest, most structured intro to LLMs you could follow, straight from the Stanford Autumn 2025 curriculum.
📚 𝗧𝗼𝗽𝗶𝗰𝘀 𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝗶𝗻𝗰𝗹𝘂𝗱𝗲:
• How Transformers actually work (tokenization, attention, embeddings)
• Decoding strategies & MoEs
• LLM finetuning (LoRA, RLHF, supervised)
• Evaluation techniques (LLM-as-a-judge)
• Optimization tricks (RoPE, quantization, approximations)
• Reasoning & scaling
• Agentic workflows (RAG, tool calling)
🧠 My workflow: I usually take the transcripts, feed them into NotebookLM, and once I’ve done the lectures, I replay them during walks or commutes. That combo works wonders for retention.
🎥 Watch these now:
- Lecture 1: https://lnkd.in/dDER-qyp
- Lecture 2: https://lnkd.in/dk-tGUDm
- Lecture 3: https://lnkd.in/drAPdjJY
- Lecture 4: https://lnkd.in/e_RSgMz7
- Lecture 5: https://lnkd.in/eivMA9pe
- Lecture 6: https://lnkd.in/eYwwwMXn
- Lecture 7: https://lnkd.in/eKwkEDXV
- Lecture 8: https://lnkd.in/eEWvyfyK
- Lecture 9: https://lnkd.in/euiKRGaQ
🗓 Do yourself a favor for this 2026: block 2-3 hours per week / llectue and go through them.
If you’re in AI — whether building infra, agents, or apps — this is the foundational course you don’t want to miss.
Let’s level up.
https://t.me/CodeProgrammer 😅
Просто зацените: парень показывает внутрянку крупных брендов, как компании вечно водят вас за нос и заставляют тратить деньги на безделушки и Лабубу
Ничего не продает, просто куча трушных постов про маркетинг и конечно же мемы (а куда без них). Читайте: @maratyus
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Start 2026 with a submitted paper—not just a plan
Repost from ADMINOTEKA
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1. What will be the output of the following code?
def add_item(item, lst=None):
if lst is None:
lst = []
lst.append(item)
return lst
print(add_item(1))
print(add_item(2))
A. [1] then [2]
B. [1] then [1, 2]
C. [] then []
D. Raises TypeError
Correct answer: A.
2. What is printed by this code?
x = 10
def func():
print(x)
x = 5
func()
A. 10
B. 5
C. None
D. UnboundLocalError
Correct answer: D.
3. What is the result of executing this code?
a = [1, 2, 3]
b = a[:]
a.append(4)
print(b)
A. [1, 2, 3, 4]
B. [4]
C. [1, 2, 3]
D. []
Correct answer: C.
4. What does the following expression evaluate to?
bool("False")
A. False
B. True
C. Raises ValueError
D. None
Correct answer: B.
5. What will be the output?
print(type({}))
A. <class 'list'>
B. <class 'set'>
C. <class 'dict'>
D. <class 'tuple'>
Correct answer: C.
6. What is printed by this code?
x = (1, 2, [3])
x[2] += [4]
print(x)
A. (1, 2, [3])
B. (1, 2, [3, 4])
C. TypeError
D. AttributeError
Correct answer: C.
7. What does this code output?
print([i for i in range(3) if i])
A. [0, 1, 2]
B. [1, 2]
C. [0]
D. []
Correct answer: B.
8. What will be printed?
d = {"a": 1}
print(d.get("b", 2))
A. None
B. KeyError
C. 2
D. "b"
Correct answer: C.
9. What is the output?
print(1 in [1, 2], 1 is 1)
A. True True
B. True False
C. False True
D. False False
Correct answer: A.
10. What does this code produce?
def gen():
for i in range(2):
yield i
g = gen()
print(next(g), next(g))
A. 0 1
B. 1 2
C. 0 0
D. StopIteration
Correct answer: A.
11. What is printed?
print({x: x*x for x in range(2)})
A. {0, 1}
B. {0: 0, 1: 1}
C. [(0,0),(1,1)]
D. Error
Correct answer: B.
12. What is the result of this comparison?
print([] == [], [] is [])
A. True True
B. False False
C. True False
D. False True
Correct answer: C.
13. What will be printed?
def f():
try:
return "A"
finally:
print("B")
print(f())
A. A
B. B
C. B then A
D. A then B
Correct answer: C.
14. What does this code output?
x = [1, 2]
y = x
x = x + [3]
print(y)
A. [1, 2, 3]
B. [3]
C. [1, 2]
D. Error
Correct answer: C.
15. What is printed?
print(type(i for i in range(3)))
A. <class 'list'>
B. <class 'tuple'>
C. <class 'generator'>
D. <class 'range'>
Correct answer: C.Repost from Machine Learning
100+ LLM Interview Questions and Answers (GitHub Repo)
Anyone preparing for #AI/#ML Interviews, it is mandatory to have good knowledge related to #LLM topics.
This# repo includes 100+ LLM interview questions (with answers) spanning over LLM topics like
LLM Inference
LLM Fine-Tuning
LLM Architectures
LLM Pretraining
Prompt Engineering
etc.
🖕 Github Repo - https://github.com/KalyanKS-NLP/LLM-Interview-Questions-and-Answers-Hub
https://t.me/DataScienceM ✅
现已上线!2025 年 Telegram 研究 — 年度关键洞察 
