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Machine Learning with Python

Machine Learning with Python

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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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๐Ÿ“ˆ Analytical overview of Telegram channel Machine Learning with Python

Channel Machine Learning with Python (@codeprogrammer) in the English language segment is an active participant. Currently, the community unites 67 818 subscribers, ranking 2 429 in the Education category and 5 036 in the India region.

๐Ÿ“Š Audience metrics and dynamics

Since its creation on ะฝะตะฒั–ะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 67 818 subscribers.

According to the latest data from 14 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 66 over the last 30 days and by 5 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 4.52%. Within the first 24 hours after publication, content typically collects 1.70% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 3 064 views. Within the first day, a publication typically gains 1 155 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 5.
  • Thematic interests: Content is focused on key topics such as insidead, learning, degree, evaluation, algorithm.

๐Ÿ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
โ€œLearn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikhoโ€

Thanks to the high frequency of updates (latest data received on 15 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

67 818
Subscribers
+524 hours
No data7 days
+6630 days
Posts Archive
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python

import PyPDF2
import pyttsx3
# Read the pdf by specifying the path in your computer 
pdfReader = PyPDF2.PdfFileReader(open('clcoding.pdf', 'rb'))
# Get the handle to speaker
speaker = pyttsx3.init()
# split the pages and read one by one
for page_num in range(pdfReader.numPages): 
    text = pdfReader.getPage(page_num).extractText() 
    speaker.say(text)
    speaker.runAndWait()
# stop the speaker after completion 
speaker.stop() 
# save the audiobook at specified path 
engine.save_to_file(text, 'E:\audio.mp3') 
engine.runAndWait()

๐Ÿ”… Create an Audiobook in Python
๐Ÿ”… Create an Audiobook in Python

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๐ŸŒดMachine Learning A-Zโ„ข: AI, Python & R + ChatGPT Bonus [2023] ๐ŸŒด Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included. This course includes: 42 hours on-demand video 5 coding exercises 39 articles 9 downloadable resources ๐ŸŸข Price :- $20 Source :- https://www.udemy.com/course/machinelearning/ Contact @hussein_sheikho

๐Ÿ–ฅ Face swap on python. pip install --user opencv-python insightface matplotlib # ัะบะฐั‡ะฐะน 'inswapper_128.onnx' ะพั‚ััŽะดะฐ 'clck.ru
๐Ÿ–ฅ Face swap on python.
pip install --user opencv-python insightface matplotlib # ัะบะฐั‡ะฐะน 'inswapper_128.onnx' ะพั‚ััŽะดะฐ 'clck.ru/36ct6v' ะธ ะทะฐะบะธะฝัŒ ะฒ ะฟะฐะฟะบัƒ ั ะบะพะดะพะผ import cv2 import insightface from insightface.app import FaceAnalysis import matplotlib.pyplot as plt app = FaceAnalysis(name='buffalo_l') app.prepare(ctx_id=0, det_size=(640, 640)) swapper = insightface.model_zoo.get_model('inswapper_128.onnx') def swap_faces(img): img = cv2.imread(img) fig, ax = plt.subplots(1, 1, figsize=(10, 5)) ax.imshow(img[:,:,::-1]) ax.axis('off'); plt.show() faces = app.get(img) face1, face2 = faces[0], faces[1] imgnew = img.copy() imgnew = swapper.get(imgnew, face1, face2, paste_back=True) imgnew = swapper.get(imgnew, face2, face1, paste_back=True) fig, ax = plt.subplots(1, 1, figsize=(10, 5)) ax.imshow(imgnew[:,:,::-1]) ax.axis('off'); plt.show() return imgnew swap_faces('ะขะฃะข ะขะ’ะžะ ะ˜ะ—ะžะ‘ะ ะะ–ะ•ะะ˜ะ•')
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