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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网络:Machine Learning with Python 印度5 035 教育2 428...

📈 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 67 833 名订阅者，在教育类别中位列第 2 428，并在印度地区排名第 5 035 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 67 833 名订阅者。

根据 15 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 82，过去 24 小时变化为 13，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 4.40%。内容发布后 24 小时内通常能获得 1.74% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 2 983 次浏览，首日通常累积 1 177 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 5。
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 16 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。

67 833

订阅者

+1324 小时

+187 天

+8230 天

2 983

帖子浏览量

~ 1 17724 小时

~ 1 70948 小时

4.40%

参与率

~ 4

每日帖子数

Ads index

beta

帖子存档

67 833

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🦙 Code Llama The most powerful AI assistant for writing Python code. • Github: https://github.com/facebookresearch/codellama • Docs: https://ai.meta.com/blog/code-llama-large-language-model-coding/ • Post: https://ai.meta.com/blog/code-llama-large-language-model-coding/ https://t.me/CodeProgrammer

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👁‍🗨 Running YOLOv7 algorithm on your webcam using Ikomia API

from ikomia.dataprocess.workflow import Workflow
from ikomia.utils import ik
from ikomia.utils.displayIO import display
import cv2

stream = cv2.VideoCapture(0)

# Init the workflow
wf = Workflow()

# Add color conversion
cvt = wf.add_task(ik.ocv_color_conversion(code=str(cv2.COLOR_BGR2RGB)), auto_connect=True)

# Add YOLOv7 detection
yolo = wf.add_task(ik.infer_yolo_v7(conf_thres="0.7"), auto_connect=True)

while True:
    ret, frame = stream.read()
    
    # Test if streaming is OK
    if not ret:
        continue

    # Run workflow on image
    wf.run_on(frame)

    # Display results from "yolo"
    display(
        yolo.get_image_with_graphics(),
        title="Object Detection - press 'q' to quit",
        viewer="opencv"
    )

    # Press 'q' to quit the streaming process
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# After the loop release the stream object
stream.release()

# Destroy all windows
cv2.destroyAllWindows()

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👁‍🗨 Running YOLOv7 algorithm on your webcam using Ikomia API

67 833

Join us for an exhilarating Python Singula Meetup Online! 🌐🎉 We bring together Python enthusiasts, developers, and learners from around the world! 🗓️ Date: August 29 ⏰Time: 6:00pm CET 🔗 To sign up 📺 The meetup will be broadcasted via YouTube! Our lineup of esteemed speakers will dive into exciting topics: ▶Discover the reasons behind Python's occasional slowness in specific tasks ▶Learn practical strategies to secure a Hadoop cluster within a large ML team ▶Unleash the power of spatial data with expert feature engineering 📌Subscribe to our Telegram channel, where you can find all the latest announcements for upcoming meetups!

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Introduction to Python

Learn fundamental concepts for Python beginners that will help you get started on your journey to learn Python. These tutorials focus on the absolutely essential things you need to know about Python.

What You’ll Learn: • Installing a Python environment • The basics of the Python language https://realpython.com/learning-paths/python3-introduction/ https://t.me/CodeProgrammer

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🔭 Daily Useful Scripts Daily.py is a repository that provides a collection of ready-to-use Python scripts for automating common daily tasks. git clone https://github.com/Chamepp/Daily.py.git ▪ Github: https://github.com/Chamepp/Daily.py https://t.me/CodeProgrammer

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🖥 5 useful Python automation scripts 1. Download Youtube videos pip install pytube

from pytube import YouTube

# Specify the URL of the YouTube video
video_url = "https://www.youtube.com/watch?v=dQw4w9WgXcQ"

# Create a YouTube object
yt = YouTube(video_url)

# Select the highest resolution stream
stream = yt.streams.get_highest_resolution()

# Define the output path for the downloaded video
output_path = "path/to/output/directory/"

# Download the video
stream.download(output_path)

print("Video downloaded successfully!")

2. Automate WhatsApp messages pip install pywhatkit

import pywhatkit

# Set the target phone number (with country code) and the message
phone_number = "+1234567890"
message = "Hello, this is an automated WhatsApp message!"

# Schedule the message to be sent at a specific time (24-hour format)
hour = 13
minute = 30

# Send the scheduled message
pywhatkit.sendwhatmsg(phone_number, message, hour, minute)

3. Google search with Python


pip install googlesearch-python

from googlesearch import search

# Define the query you want to search
query = "Python programming"

# Specify the number of search results you want to retrieve
num_results = 5

# Perform the search and retrieve the results
search_results = search(query, num_results=num_results, lang='en')

# Print the search results
for result in search_results:
    print(result)

4. Download Instagram posts pip install instaloader

import instaloader

# Create an instance of Instaloader
loader = instaloader.Instaloader()

# Define the target Instagram profile
target_profile = "instagram"

# Download posts from the profile
loader.download_profile(target_profile, profile_pic=False, fast_update=True)

print("Posts downloaded successfully!")

5. Extract audio from video files pip install moviepy


from moviepy.editor import VideoFileClip

# Define the path to the video file
video_path = "path/to/video/file.mp4"

# Create a VideoFileClip object
video_clip = VideoFileClip(video_path)

# Extract the audio from the video
audio_clip = video_clip.audio

# Define the output audio file path
output_audio_path = "path/to/output/audio/file.mp3"

# Write the audio to the output file
audio_clip.write_audiofile(output_audio_path)

# Close the clips
video_clip.close()
audio_clip.close()

print("Audio extracted successfully!")

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Best-of Python 🏆 A ranked list of awesome Python open-source libraries & tools. Updated weekly. ▪ Github: https://github.com/ml-tooling/best-of-python https://t.me/CodeProgrammer More reaction. please ⭐️💐⭐️

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🖥 Text-to-Speech with PyTorch

import torchaudio
import torch
import matplotlib.pyplot as plt
import IPython.display

bundle = torchaudio.pipelines.TACOTRON2_WAVERNN_PHONE_LJSPEECH

processor = bundle.get_text_processor()
tacotron2 = bundle.get_tacotron2().to(device)  # Move model to the desired device
vocoder = bundle.get_vocoder().to(device)      # Move model to the desired device

text = " My first text to speech!"

with torch.inference_mode():
    processed, lengths = processor(text)
    processed = processed.to(device)      # Move processed text data to the device
    lengths = lengths.to(device)          # Move lengths data to the device
    spec, spec_lengths, _ = tacotron2.infer(processed, lengths)
    waveforms, lengths = vocoder(spec, spec_lengths)

    
fig, [ax1, ax2] = plt.subplots(2, 1, figsize=(16, 9))
ax1.imshow(spec[0].cpu().detach(), origin="lower", aspect="auto")  # Display the generated spectrogram
ax2.plot(waveforms[0].cpu().detach())                             # Display the generated waveform7. Play the generated audio using IPython.display.Audio
IPython.display.Audio(waveforms[0:1].cpu(), rate=vocoder.sample_rate)

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🖥 Text-to-Speech with PyTorch https://t.me/CodeProgrammer

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