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

Network:Machine Learning with Python India5 049 Education2 404...

📈 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 67 819 subscribers, ranking 2 404 in the Education category and 5 049 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 67 819 subscribers.

According to the latest data from 05 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 77 over the last 30 days and by 9 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 2.60%. Within the first 24 hours after publication, content typically collects 2.50% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 767 views. Within the first day, a publication typically gains 1 695 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 6.
Thematic interests: Content is focused on key topics such as insidead, learning, degree, evaluation, algorithm.

📝 Description and content policy

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

Thanks to the high frequency of updates (latest data received on 06 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.

67 819

Subscribers

+924 hours

+587 days

+7730 days

1 767

Post views

~ 1 69524 hours

~ 2 25748 hours

2.60%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

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🧠 Converting images to ASCII: text instead of pixels Want to turn any image into ASCII art? It's not magic, just simple brightness processing. It's tedious and stupid to do it manually

img = [
    [255, 0, 0],
    [0, 255, 0]
]
# Now we need to pick a symbol for each pixel...
# What a hassle.

Problem: Manually selecting symbols by brightness is a pain. We need to automate the conversion of grayscale to symbols. ✔️ The right way (using gradation)

```python
from PIL import Image

def image_to_ascii(path, width=100):
    img = Image.open(path)
    aspect = img.height / img.width
    height = int(width * aspect * 0.55)
    img = img.resize((width, height)).convert('L')

    ascii_chars = '@%#*+=-:. '
    pixels = img.getdata()

    ascii_art = '\n'.join(
        ascii_chars[pixel * (len(ascii_chars) - 1) // 255]
        for pixel in pixels
    )
    lines = [ascii_art[i:i+width] for i in range(0, len(ascii_art), width)]
    return '\n'.join(lines)

print(image_to_ascii('

cat.jpg'))``` How it works: convert('L') converts the image to grayscale Each pixel (0-255) is assigned a symbol from the set The darker the pixel, the "denser" the symbol (e.g., '@'), the lighter - the "weaker" (space) Let's write a converter with customiz

able palette:
```python
class AsciiConverter:
    PALETTES = {
        'default&#39: '@%#*+=-:. ',
        'blocks&#39: '█rayed ',
        'detailed&#39: '$@B%8&WM#*oahkbdpqwmZO0QLCJUYXzcvunxrjft/\\|()1{}[]?-_+~<>i!lI;:,"^`\'. '
    }

    def __init__(self, palette_name='default&#39):
        if palette_name not in self.PALETTES:
            raise ValueError(f'Нет такой палитры, идиот. Выбери из: {list(self.PALETTES.keys())}')
        self.chars = self.PALETTES[palette_name]

    def convert(self, image_path, width=80):
        # ... code to convert using self.chars ...

return ascii_result``` Try specifying a non-existent palette - you'll get a cl

ear error. Key parameters: 🔵Width - determines the size of the final ASCII art 🔵Character palette - affects the detail and style 🔵Aspect ratio - important for correct display 🔵Inversion - you can invert the bright

ness for a dark background Important: ASCII art isn't just a fun thing. It's used to visualize data in the console, create creative logs, and even "hide" information in plain sight. 👩‍💻 @CodeProgrammer

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free

67 816

Repost from Udemy Free Coupons

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67 816

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67 816

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67 816

Repost from ADMINOTEKA

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67 816

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𝗧𝗵𝗼𝘀𝗲 𝘄𝗵𝗼 𝘄𝗮𝗻𝘁 𝗿𝗲𝗳𝗲𝗿𝗿𝗮𝗹𝘀 𝗮𝗻𝗱 𝗝𝗼𝗯𝘀 𝗮𝗻𝗱 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽𝘀 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀 𝗳𝗿𝗼𝗺 𝗧𝗼𝗽 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗕𝗮𝘀𝗲𝗱, 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗕𝗮𝘀𝗲𝗱 𝗮𝗻𝗱 𝗦𝘁𝗮𝗿𝘁 𝘂𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗹𝗶𝗸𝗲 𝗔𝗺𝗮𝘇𝗼𝗻, 𝗚𝗼𝗼𝗴𝗹𝗲, 𝗔𝗽𝗽𝗹𝗲, 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁, 𝗜𝗕𝗠, 𝗧𝗖𝗦, 𝗖𝗼𝗴𝗻𝗶𝘇𝗮𝗻𝘁, 𝗪𝗶𝗽𝗿𝗼, 𝗖𝗧𝗦, 𝗚𝗼𝗹𝗱𝗺𝗮𝗻 𝗦𝗮𝗰𝗵𝘀, 𝗢𝗹𝗮, 𝗨𝗯𝗲𝗿, 𝗭𝗼𝗺𝗮𝘁𝗼, 𝗦𝘄𝗶𝗴𝗴𝘆, 𝘂𝗽𝗚𝗿𝗮𝗱, 𝗖𝘂𝗿𝗲 𝗙𝗶𝘁, 𝗛𝗮𝗰𝗸𝗲𝗿𝗿𝗮𝗻𝗸, 𝗚𝗲𝗲𝗸𝘀𝗳𝗼𝗿𝗴𝗲𝗲𝗸𝘀 𝗮𝗻𝗱 𝗺𝗮𝗻𝘆 𝗺𝗼𝗿𝗲, 𝗰𝗮𝗻 𝗷𝗼𝗶𝗻 𝘁𝗵𝗲𝘀𝗲 𝗯𝗲𝗹𝗼𝘄 𝗰𝗵𝗮𝗻𝗻𝗲𝗹𝘀 👇👇 https://t.me/jobsandinternshipsupdates

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💛 Top 10 Best Websites to Learn Machine Learning ⭐️ by [@codeprogrammer] --- 🧠 Google’s ML Course 🔗 https://developers.google.com/machine-learning/crash-course 📈 Kaggle Courses 🔗 https://kaggle.com/learn 🧑‍🎓 Coursera – Andrew Ng’s ML Course 🔗 https://coursera.org/learn/machine-learning ⚡️ Fast.ai 🔗 https://fast.ai 🔧 Scikit-Learn Documentation 🔗 https://scikit-learn.org 📹 TensorFlow Tutorials 🔗 https://tensorflow.org/tutorials 🔥 PyTorch Tutorials 🔗 https://docs.pytorch.org/tutorials/ 🏛️ MIT OpenCourseWare – Machine Learning 🔗 https://ocw.mit.edu/courses/6-867-machine-learning-fall-2006/ ✍️ Towards Data Science (Blog) 🔗 https://towardsdatascience.com --- 💡 Which one are you starting with? Drop a comment below! 👇 #MachineLearning #LearnML #DataScience #AI #TechWithRam

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