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Machine Learning with Python

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

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📈 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

📊 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 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。

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68 103
订阅者
-1824 小时
-907
+7530
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

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These questions are taken from the book "Python Workout 2025"

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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.

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Repost from Machine Learning
100+ LLM Interview Questions and Answers (GitHub Repo) Anyone preparing for #AI/#ML Interviews, it is mandatory to have good
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

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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/CodeProgrammer

Automate the Boring Stuff with Python Workbook 2025 The Best Book to Learn Python is available on our collection on Signal ht
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This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visua
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_rRW2scgfRhOTc0https://t.me/Codeprogrammer

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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

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I rarely say this, but this is the best repository for mastering Python. The course is led by David Beazley, the author of Py
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

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