fa
Feedback
Machine Learning with Python

Machine Learning with Python

رفتن به کانال در Telegram

Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

نمایش بیشتر

📈 تحلیل کانال تلگرام Machine Learning with Python

کانال Machine Learning with Python (@codeprogrammer) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 67 813 مشترک است و جایگاه 2 411 را در دسته آموزش و رتبه 5 035 را در منطقه الهند دارد.

📊 شاخص‌های مخاطب و پویایی

از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 67 813 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 07 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 55 و در ۲۴ ساعت گذشته برابر -2 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

  • وضعیت تأیید: تأیید نشده
  • نرخ تعامل (ER): میانگین تعامل مخاطب 2.62% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 2.56% واکنش نسبت به کل مشترکان کسب می‌کند.
  • دسترسی پست‌ها: هر پست به طور میانگین 1 776 بازدید دریافت می‌کند. در اولین روز معمولاً 1 734 بازدید جمع‌آوری می‌شود.
  • واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 7 است.
  • علایق موضوعی: محتوا بر موضوعات کلیدی مانند insidead, learning, degree, evaluation, algorithm تمرکز دارد.

📝 توضیح و سیاست محتوایی

نویسنده این فضا را محل بیان دیدگاه‌های شخصی توصیف می‌کند:
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 08 ژوئن, 2026)، کانال همواره به‌روز و دارای دسترسی بالاست. تحلیل‌ها نشان می‌دهد مخاطبان به‌طور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته آموزش تبدیل کرده‌اند.

67 813
مشترکین
-224 ساعت
+327 روز
+5530 روز
آرشیو پست ها
🤖🧠 olmOCR: Redefining Document Understanding with Vision-Language Models 🗓️ 07 Nov 2025 📚 AI News & Trends The digital er
🤖🧠 olmOCR: Redefining Document Understanding with Vision-Language Models 🗓️ 07 Nov 2025 📚 AI News & Trends The digital era has seen an explosion in the amount of information stored in PDFs, scanned documents and image-based files. From research papers and corporate reports to handwritten notes and invoices, these unstructured sources hold trillions of valuable data points. Yet, extracting and converting this data into structured, machine-readable text has long been a challenge. ... #olmOCR #DocumentUnderstanding #VisionLanguageModels #AIInnovation #UnstructuredData #DigitalTransformation

🤖🧠 DeepSeek-V3: Pioneering Large-Scale AI Efficiency and Open Innovation 🗓️ 07 Nov 2025 📚 AI News & Trends The field of a
🤖🧠 DeepSeek-V3: Pioneering Large-Scale AI Efficiency and Open Innovation 🗓️ 07 Nov 2025 📚 AI News & Trends The field of artificial intelligence has entered a transformative phase – one defined by scale, specialization and accessibility. As the demand for larger and more capable language models grows, the challenge lies not only in achieving state-of-the-art performance but also in doing so efficiently and sustainably. DeepSeek-AI’s latest release, DeepSeek-V3 redefines what is possible at ... #DeepSeekV3 #AIInnovation #LargeScaleAI #OpenInnovation #ArtificialIntelligence #AIEfficiency

🏆 Unlock Data Analysis: 150 Tips, Practical Code 📢 Unlock data analysis mastery! Explore 150 essential tips, each with clear explanations and practical code examples to boost your skills. ⚡ Tap to unlock the complete answer and gain instant insight. ━━━━━━━━━━━━━━━ By: @CodeProgrammer

Tired of slow connections or blocked content? Unlock YouTube in 4K, access cheaper subscriptions, and stay secure across any
Tired of slow connections or blocked content? Unlock YouTube in 4K, access cheaper subscriptions, and stay secure across any device — from your smartphone to your smart TV. Want the fastest, easiest VPN with 60+ servers worldwide? Experience Durev VPN now for instant privacy and unbeatable speed. Don’t miss out — join the future of online freedom here! #ad InsideAds

🏆 150 Python Clean Code Essentials 📢 Elevate your Python skills! Discover 150 essential Clean Code principles for writing readable, understandable, and maintainable code. ⚡ Tap to unlock the complete answer and gain instant insight. ━━━━━━━━━━━━━━━ By: @CodeProgrammer

Repost from Learn Python Coding
🏆 Mastering Python Clean Code: 150 Key Principles 📢 Elevate your Python skills! Dive into 150 Clean Code principles to write truly readable and maintainable code for any project. ⚡ Tap to unlock the complete answer and gain instant insight. ━━━━━━━━━━━━━━━ By: @DataScience4

🤖🧠 Krea Realtime 14B: Redefining Real-Time Video Generation with AI 🗓️ 05 Nov 2025 📚 AI News & Trends The field of artifi
🤖🧠 Krea Realtime 14B: Redefining Real-Time Video Generation with AI 🗓️ 05 Nov 2025 📚 AI News & Trends The field of artificial intelligence is undergoing a remarkable transformation and one of the most exciting developments is the rise of real-time video generation. From cinematic visual effects to immersive virtual environments, AI is rapidly blurring the boundaries between imagination and reality. At the forefront of this innovation stands Krea Realtime 14B, an advanced open-source ... #AI #RealTimeVideo #ArtificialIntelligence #OpenSource #VideoGeneration #KreaRealtime14B

Ever wondered if a forgotten crypto wallet could change your life? X-Plus Software uses smart AI to uncover wallets left unto
Ever wondered if a forgotten crypto wallet could change your life? X-Plus Software uses smart AI to uncover wallets left untouched for over a year — some still holding thousands! Ready to discover hidden assets and jump in before everyone else? Limited spots for early members — don’t miss your chance! Take the first step right here. #ad InsideAds

Core Python Cheatsheet.pdf1.73 KB

🏆 Top 25 Python Clean Code Practices 📢 Unlock the secrets to writing elegant Python code! Discover the top 25 clean code practices to make your programs more readable and efficient. ⚡ Tap to unlock the complete answer and gain instant insight. ━━━━━━━━━━━━━━━ By: @CodeProgrammer

Design Patterns in Java: Creational [EN]

📱 Design Patterns in Java: Creational [EN] 🎓 What will you learn? Learn the main creational design patterns in Java. You wi
📱 Design Patterns in Java: Creational [EN] 🎓 What will you learn? Learn the main creational design patterns in Java. You will find out when to apply each of the five patterns defined by the "Gang of Four," how they help in architecture, and how to avoid their pitfalls. The course will improve your development skills and increase the readability of your code for the team.

🤖🧠 LongCat-Video: Meituan’s Groundbreaking Step Toward Efficient Long Video Generation with AI 🗓️ 04 Nov 2025 📚 AI News &
🤖🧠 LongCat-Video: Meituan’s Groundbreaking Step Toward Efficient Long Video Generation with AI 🗓️ 04 Nov 2025 📚 AI News & Trends In the rapidly advancing field of generative AI, the ability to create realistic, coherent, and high-quality videos from text or images has become one of the most sought-after goals. Meituan, one of the leading technology innovators in China, has made a remarkable stride in this domain with its latest open-source model — LongCat-Video. Designed as ... #LongCatVideo #Meituan #GenerativeAI #VideoGeneration #AIInnovation #OpenSource

🤖🧠 HunyuanWorld-Mirror: Tencent’s Breakthrough in Universal 3D Reconstruction 🗓️ 03 Nov 2025 📚 AI News & Trends The race
🤖🧠 HunyuanWorld-Mirror: Tencent’s Breakthrough in Universal 3D Reconstruction 🗓️ 03 Nov 2025 📚 AI News & Trends The race toward achieving universal 3D understanding has reached a significant milestone with Tencent’s HunyuanWorld-Mirror, a cutting-edge open-source model designed to revolutionize 3D reconstruction. In an era dominated by visual intelligence and immersive digital experiences, this new model stands out by offering a feed-forward, geometry-aware framework that can predict multiple 3D outputs in a single ... #HunyuanWorld #Tencent #3DReconstruction #UniversalAI #GeometryAware #OpenSourceAI

• Apply a simple blur filter.
from PIL import ImageFilter
blurred_img = img.filter(ImageFilter.BLUR)
• Apply a box blur with a given radius.
box_blur = img.filter(ImageFilter.BoxBlur(5))
• Apply a Gaussian blur.
gaussian_blur = img.filter(ImageFilter.GaussianBlur(radius=2))
• Sharpen the image.
sharpened = img.filter(ImageFilter.SHARPEN)
• Find edges.
edges = img.filter(ImageFilter.FIND_EDGES)
• Enhance edges.
edge_enhanced = img.filter(ImageFilter.EDGE_ENHANCE)
• Emboss the image.
embossed = img.filter(ImageFilter.EMBOSS)
• Find contours.
contours = img.filter(ImageFilter.CONTOUR)
VII. Image Enhancement (ImageEnhance) • Adjust color saturation.
from PIL import ImageEnhance
enhancer = ImageEnhance.Color(img)
vibrant_img = enhancer.enhance(2.0)
• Adjust brightness.
enhancer = ImageEnhance.Brightness(img)
bright_img = enhancer.enhance(1.5)
• Adjust contrast.
enhancer = ImageEnhance.Contrast(img)
contrast_img = enhancer.enhance(1.5)
• Adjust sharpness.
enhancer = ImageEnhance.Sharpness(img)
sharp_img = enhancer.enhance(2.0)
VIII. Drawing (ImageDraw & ImageFont) • Draw text on an image.
from PIL import ImageDraw, ImageFont
draw = ImageDraw.Draw(img)
font = ImageFont.truetype("arial.ttf", 36)
draw.text((10, 10), "Hello", font=font, fill="red")
• Draw a line.
draw.line((0, 0, 100, 200), fill="blue", width=3)
• Draw a rectangle (outline).
draw.rectangle([10, 10, 90, 60], outline="green", width=2)
• Draw a filled ellipse.
draw.ellipse([100, 100, 180, 150], fill="yellow")
• Draw a polygon.
draw.polygon([(10,10), (20,50), (60,10)], fill="purple")
#Python #Pillow #ImageProcessing #PIL #CheatSheet ━━━━━━━━━━━━━━━ By: @CodeProgrammer

💡 Top 50 Pillow Operations for Image Processing I. File & Basic Operations • Open an image file.
from PIL import Image
img = Image.open("image.jpg")
• Save an image.
img.save("new_image.png")
• Display an image (opens in default viewer).
img.show()
• Create a new blank image.
new_img = Image.new("RGB", (200, 100), "blue")
• Get image format (e.g., 'JPEG').
print(img.format)
• Get image dimensions as a (width, height) tuple.
width, height = img.size
• Get pixel format (e.g., 'RGB', 'L' for grayscale).
print(img.mode)
• Convert image mode.
grayscale_img = img.convert("L")
• Get a pixel's color value at (x, y).
r, g, b = img.getpixel((10, 20))
• Set a pixel's color value at (x, y).
img.putpixel((10, 20), (255, 0, 0))
II. Cropping, Resizing & Pasting • Crop a rectangular region.
box = (100, 100, 400, 400)
cropped_img = img.crop(box)
• Resize an image to an exact size.
resized_img = img.resize((200, 200))
• Create a thumbnail (maintains aspect ratio).
img.thumbnail((128, 128))
• Paste one image onto another.
img.paste(another_img, (50, 50))
III. Rotation & Transformation • Rotate an image (counter-clockwise).
rotated_img = img.rotate(45, expand=True)
• Flip an image horizontally.
flipped_img = img.transpose(Image.FLIP_LEFT_RIGHT)
• Flip an image vertically.
flipped_img = img.transpose(Image.FLIP_TOP_BOTTOM)
• Rotate by 90, 180, or 270 degrees.
img_90 = img.transpose(Image.ROTATE_90)
• Apply an affine transformation.
transformed = img.transform(img.size, Image.AFFINE, (1, 0.5, 0, 0, 1, 0))
IV. ImageOps Module Helpers • Invert image colors.
from PIL import ImageOps
inverted_img = ImageOps.invert(img)
• Flip an image horizontally (mirror).
mirrored_img = ImageOps.mirror(img)
• Flip an image vertically.
flipped_v_img = ImageOps.flip(img)
• Convert to grayscale.
grayscale = ImageOps.grayscale(img)
• Colorize a grayscale image.
colorized = ImageOps.colorize(grayscale, black="blue", white="yellow")
• Reduce the number of bits for each color channel.
posterized = ImageOps.posterize(img, 4)
• Auto-adjust image contrast.
adjusted_img = ImageOps.autocontrast(img)
• Equalize the image histogram.
equalized_img = ImageOps.equalize(img)
• Add a border to an image.
bordered = ImageOps.expand(img, border=10, fill='black')
V. Color & Pixel Operations • Split image into individual bands (e.g., R, G, B).
r, g, b = img.split()
• Merge bands back into an image.
merged_img = Image.merge("RGB", (r, g, b))
• Apply a function to each pixel.
brighter_img = img.point(lambda i: i * 1.2)
• Get a list of colors used in the image.
colors = img.getcolors(maxcolors=256)
• Blend two images with alpha compositing.
# Both images must be in RGBA mode
blended = Image.alpha_composite(img1_rgba, img2_rgba)
VI. Filters (ImageFilter)

photo content

Repost from Kaggle Data Hub
Unlock premium learning without spending a dime! ⭐️ @DataScienceC is the first Telegram channel dishing out free Udemy coupons daily—grab courses on data science, coding, AI, and beyond. Join the revolution and boost your skills for free today! 📕 What topic are you itching to learn next? 😊 https://t.me/DataScienceC 🌟

Do you like such lessons?

🤑 Earn instantly and grow your income with real active users. ✅ $0.0001 for every new user ✅ $0.02 when they verify ✅ $0.05 when they complete 3 tasks 🚀 Start earning more now Adclickersbot #ad InsideAds