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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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📈 Análisis del canal de Telegram Machine Learning with Python

El canal Machine Learning with Python (@codeprogrammer) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 67 813 suscriptores, ocupando la posición 2 411 en la categoría Educación y el puesto 5 035 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 67 813 suscriptores.

Según los últimos datos del 07 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 55, y en las últimas 24 horas de -2, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.62%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.56% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 1 776 visualizaciones. En el primer día suele acumular 1 734 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 7.
  • Intereses temáticos: El contenido se centra en temas clave como insidead, learning, degree, evaluation, algorithm.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 08 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.

67 813
Suscriptores
-224 horas
+327 días
+5530 días
Archivo de publicaciones
🤖🧠 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

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🤖🧠 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

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🏆 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

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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
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• 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
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