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 68 103 名订阅者,在 教育 类别中位列第 2 374,并在 印度 地区排名第 4 765 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 68 103 名订阅者。
根据 28 八月, 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 75,过去 24 小时变化为 -18,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 4.69%。内容发布后 24 小时内通常能获得 1.70% 的反应,占订阅者总量。
- 帖子覆盖: 每篇帖子平均可获得 3 194 次浏览,首日通常累积 1 155 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 29 八月, 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。
68 103
订阅者
-1824 小时
-907 天
+7530 天
帖子存档
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
🔥 NEW YEAR 2026 – PREMIUM SCIENTIFIC PAPER WRITING OFFER 🔥
Q1-Ready | Journal-Targeted | Publication-Focused
Serious researchers, PhD & MSc students, postdocs, universities, and funded startups only.
To start 2026 strong, we’re offering a limited New Year scientific writing package designed for fast-track publication, not academic busywork.
🎯 What We Offer (End-of-Year Special):
✍️ Full Research Paper Writing – $400
(Q1 / Q2 journal–ready)
Includes:
✅ Journal-targeted manuscript (Elsevier / Springer / Wiley / IEEE / MDPI)
✅ IMRAD structure (Introduction–Methods–Results–Discussion)
✅ Strong problem formulation & novelty framing
✅ Methodology written to reviewer standards
✅ Professional academic English (native-level)
✅ Plagiarism-free (Turnitin <10%)
✅ Ready for immediate submission
📊 Available Paper Types:
Original Research Articles
Review & Systematic Review
AI / Machine Learning Papers
Engineering & Medical Research
Health AI & Clinical Data Studies
Interdisciplinary & Applied Research
🧠 Optional Add-ons (if needed):
Journal selection & scope matching
Cover letter to editor
Reviewer response (after review)
Statistical validation & result polishing
Figure & table redesign (publication quality)
🚀 Why This Is Different
We don’t “write generic papers.”
We engineer publishable research.
✔️ Real novelty positioning
✔️ Reviewer-proof logic
✔️ Data-driven arguments
✔️ Aligned with current 2025–2026 journal expectations
Many of our papers are built on real-world datasets and are already aligned with Q1 journal standards.
⏳ New Year Offer – Limited Time
Regular price: $1,500 – $3,000
New Year 2026 price: $400
Limited slots (quality > quantity)
🎓 Priority given to:
PhD / MSc students
Active researchers
Funded startups
Universities & labs
📩 DM for details, samples & timelines
Contact:
@Omidyzd62
Start 2026 with a submitted paper—not just a plan
Repost from ADMINOTEKA
Админотека — это лучший сервис для монетизации твоего канала!
Привет, давай знакомиться. Не самое скромное приветствие получилось, но мы можем подтвердить свои слова.
🌑 Ведь у нас зарабатывают даже самые маленькие каналы с 20 охватами
🌑 Стабильные офферы каждую неделю
🌑 Алгоритмы, рейтинг, защита сделок — и всё это автоматизировано
🌑 Удобные выплаты на привязанный кошелек
🌑 Здесь же можно и купить размещения по самому низкому прайсу на рынке
💸 Подключай свой канал и проверяй на практике. Преврати свой канал в реальный доход вместе с нами!
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 ✅
Now you can search Eveything 🎉
Your can search everything by keywords:
Channels, Chats, Bots. . .
Videos, Music, Images, Files. . .
even 🤭 18+ content 😀
Type your interests to explore !
#ad
Convert any long article or PDF into a test in a couple of seconds!
Mini-service: we take the text of the article (or extract it from
PDF), send it to GPT and receive a set of test questions with answer options and a key.
First, we load the text of the material:
# article_text — this is where we put the text of the article
with open("article.txt", "r", encoding="utf-8") as f:
article_text = f.read()
# for PDF, you can extract the text in advance with any library (PyPDF2, pdfplumber, etc.)
Next, we ask GPT to generate a test:
prompt = (
"You are an exam methodologist."
"Based on this text, create 15 test questions."
"Each question is in the format:\n"
"1) Question text\n"
"A. Option 1\n"
"B. Option 2\n"
"C. Option 3\n"
"D. Option 4\n"
"Correct answer: <letter>."
"Do not add explanations and comments, only questions, options, and correct answers."
)
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": prompt},
{"role": "user", "content": article_text}
])
print(response.choices[0].message.content.strip())
🔥 Suitable for online courses, educational centers, and corporate training — you immediately get a ready-made bank of tests from any article.
🚪 https://t.me/CodeProgrammerAutomate the Boring Stuff with Python Workbook 2025
The Best Book to Learn Python is available on our collection on Signal
https://signal.group/#CjQKIPcpEqLQow53AG7RHjeVk-4sc1TFxyym3r0gQQzV-OPpEhCPw_-kRmJ8LlC13l0WiEfp
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
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/Codeprogrammer
Now you can search Eveything 🎉
Your can search everything by keywords:
Channels, Chats, Bots. . .
Videos, Music, Images, Files. . .
even 🤭 18+ content 😀
Type your interests to explore !
#ad
The Python library PandasAI has been released for simplified data analysis using AI.
You can ask questions about the dataset in plain language directly in the AI dialogue, compare different datasets, and create graphs. It saves a lot of time, especially in the initial stage of getting acquainted with the data. It supports CSV, SQL, and Parquet.
And here's the link 😍
👉 https://t.me/DataAnalyticsX
I rarely say this, but this is the best repository for mastering Python.
The course is led by David Beazley, the author of Python Cookbook (3rd edition, O'Reilly) and Python Distilled (Addison-Wesley).
In this PythonMastery.pdf, all the information is structured
👾 Link: https://github.com/dabeaz-course/python-mastery/blob/main/PythonMastery.pdf
In the Exercises folder, all the exercises are located
👾 Link: https://github.com/dabeaz-course/python-mastery/tree/main/Exercises
In the Solutions folder — the solutions
👾 Link: https://github.com/dabeaz-course/python-mastery/tree/main/Solutions
👉 @codeprogrammer
Микро-каналы — главный тренд на рынке телеграма среди рекламодателей в этом году
Канал на пару десятков читателей есть почти у каждого, но где найти клиентов с деньгами?
Ловите главный бот сезона — ADMINOTEKA! Заявки с $$$ сами будут сыпаться к вам каждый день, выбирайте понравившиеся и публикуйте в канале.
Проще уже не будет
Machine Learning Fundamentals
A structured Machine Learning Fundamentals guide covering core concepts, intuition, math basics, ML algorithms, deep learning, and real-world workflows.
https://t.me/DataScienceM 🩷
Repost from Data Analytics
Want to get into Data Analysis?
Here are paid courses with certificates to build real skills:
1️⃣ Google Data Analytics Certificate
https://lnkd.in/dqEU-yht
2️⃣ IBM Data Science Certificate
https://lnkd.in/dQz58dY6
3️⃣ SQL Basics for Data Science
https://lnkd.in/dcFHHm28
4️⃣ Google Business Intelligence Certificate
https://lnkd.in/d4gbdF24
5️⃣ Microsoft Python Development Certificate
https://lnkd.in/dDXX_AHM
Which data skill are you focusing on now?
