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Machine Learning

Machine Learning

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Real Machine Learning β€” simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

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πŸ“ˆ Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 398 subscribers, ranking 3 324 in the Technologies & Applications category and 225 in the Syria region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 40 398 subscribers.

According to the latest data from 13 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 421 over the last 30 days and by 25 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.65%. Within the first 24 hours after publication, content typically collects 1.74% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 070 views. Within the first day, a publication typically gains 701 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 4.
  • Thematic interests: Content is focused on key topics such as distance, insidead, gpu, learning, degree.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œReal Machine Learning β€” simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho”

Thanks to the high frequency of updates (latest data received on 14 July, 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 Technologies & Applications category.

40 398
Subscribers
+2524 hours
+1547 days
+42130 days
Posts Archive
πŸ”₯ $10.000 WITH LISA! Lisa earned $200,000 in a month, and now it’s YOUR TURN! She’s made trading SO SIMPLE that anyone can d
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✨ Detecting COVID-19 in X-ray images with Keras, TensorFlow, and Deep Learning ✨ πŸ“– In this tutorial, you will learn how to a
✨ Detecting COVID-19 in X-ray images with Keras, TensorFlow, and Deep Learning ✨ πŸ“– In this tutorial, you will learn how to automatically detect COVID-19 in a hand-created X-ray image dataset using Keras, TensorFlow, and Deep Learning. Like most people in the world right now, I’m genuinely concerned about COVID-19. I find myself constantly…... 🏷️ #DeepLearning #KerasandTensorFlow #MedicalComputerVision #Tutorials

✨ COVID-19: Face Mask Detector with OpenCV, Keras/TensorFlow, and Deep Learning ✨ πŸ“– In this tutorial, you will learn how to
✨ COVID-19: Face Mask Detector with OpenCV, Keras/TensorFlow, and Deep Learning ✨ πŸ“– In this tutorial, you will learn how to train a COVID-19 face mask detector on a custom dataset with OpenCV, Keras/TensorFlow, and Deep Learning. Last month, I authored a blog post on detecting COVID-19 in X-ray images using deep learning.…... 🏷️ #DeepLearning #FaceApplications #KerasandTensorFlow #MedicalComputerVision #ObjectDetection #Tutorials

✨ OpenCV Social Distancing Detector ✨ πŸ“– In this tutorial, you will learn how to implement a COVID-19 social distancing detec
✨ OpenCV Social Distancing Detector ✨ πŸ“– In this tutorial, you will learn how to implement a COVID-19 social distancing detector using OpenCV, Deep Learning, and Computer Vision. Today’s tutorial is inspired by PyImageSearch reader Min-Jun, who emailed in asking: Hi Adrian, I’ve seen a number of…... 🏷️ #DeepLearning #MedicalComputerVision #ObjectDetection #Tutorials

✨ Implementing Approximate Nearest Neighbor Search with KD-Trees ✨ πŸ“– Table of Contents Implementing Approximate Nearest Neig
✨ Implementing Approximate Nearest Neighbor Search with KD-Trees ✨ πŸ“– Table of Contents Implementing Approximate Nearest Neighbor Search with KD-Trees Introduction to Approximate Nearest Neighbor Search Mathematical Foundation KD-Trees for Approximate Nearest Neighbor Search Construction of KD-Trees Querying with KD-Trees Step 1: Forward Traversal Step 2: Computing th... 🏷️ #ApproximateNearestNeighbor #KDTree #MachineLearning #NearestNeighborAlgorithm #Tutorial

✨ Introduction to Gradio for Building Interactive Applications ✨ πŸ“– Table of Contents Introduction to Gradio for Building Int
✨ Introduction to Gradio for Building Interactive Applications ✨ πŸ“– Table of Contents Introduction to Gradio for Building Interactive Applications What Is Gradio? High-Impact Projects Powered by Gradio AUTOMATIC1111’s Stable Diffusion Web UI oobabooga’s Text Generation Web UI The Next Generation of Gradio: What’s New in Version 5 Performance Improvements…... 🏷️ #Gradio #MachineLearning #Python #SoftwareDevelopment #Tutorial

✨ FastAPI Meets OpenAI CLIP: Build and Deploy with Docker ✨ πŸ“– Table of Contents FastAPI Meets OpenAI CLIP: Build and Deploy
✨ FastAPI Meets OpenAI CLIP: Build and Deploy with Docker ✨ πŸ“– Table of Contents FastAPI Meets OpenAI CLIP: Build and Deploy with Docker Building on FastAPI Foundations What’s Next? What Is OpenAI CLIP? How OpenAI CLIP Works: Understanding Text-Image Matching and Contrastive Learning Contrastive Pre-Training: Aligning Text and Image Embeddings Shared…... 🏷️ #AIApplications #DockerDeployment #FastAPIDevelopment #MachineLearning #Tutorial

✨ Build a Search Engine: Deploy Models and Index Data in AWS OpenSearch ✨ πŸ“– Table of Contents Build a Search Engine: Deploy
✨ Build a Search Engine: Deploy Models and Index Data in AWS OpenSearch ✨ πŸ“– Table of Contents Build a Search Engine: Deploy Models and Index Data in AWS OpenSearch Introduction What Will We Do in This Blog? Why Are We Using Vector Embeddings? What’s Coming Next? Configuring Your Development Environment Installing Docker (Required for…... 🏷️ #Docker #MachineLearning #OpenSearch #SearchEngines #SemanticSearch #Tutorial #VectorSearch

✨ Data augmentation with tf.data and TensorFlow ✨ πŸ“– In this tutorial, you will learn two methods to incorporate data augment
✨ Data augmentation with tf.data and TensorFlow ✨ πŸ“– In this tutorial, you will learn two methods to incorporate data augmentation into your tf.data pipeline using Keras and TensorFlow. A good dataset of images is vital when working with data augmentation in TensorFlow. It enables us to see how…... 🏷️ #DeepLearning #KerasandTensorFlow #Tutorials

✨ Smile detection with OpenCV, Keras, and TensorFlow ✨ πŸ“– In this tutorial, we will be building a complete end-to-end applica
✨ Smile detection with OpenCV, Keras, and TensorFlow ✨ πŸ“– In this tutorial, we will be building a complete end-to-end application that can detect smiles in a video stream in real-time using deep learning along with traditional computer vision techniques. To accomplish this task, we’ll be training the LetNet architecture…... 🏷️ #DeepLearning #KerasandTensorFlow #Tutorials

✨ Breaking captchas with deep learning, Keras, and TensorFlow ✨ πŸ“– In the past, we’ve worked with datasets that have been pre
✨ Breaking captchas with deep learning, Keras, and TensorFlow ✨ πŸ“– In the past, we’ve worked with datasets that have been pre-compiled and labeled for us β€” but what if we wanted to go about creating our own custom dataset and then training a CNN on it? In this tutorial, I’ll…... 🏷️ #DeepLearning #KerasandTensorFlow #Tutorials

✨ CycleGAN: Unpaired Image-to-Image Translation (Part 1) ✨ πŸ“– Table of Contents CycleGAN: Unpaired Image-to-Image Translation
✨ CycleGAN: Unpaired Image-to-Image Translation (Part 1) ✨ πŸ“– Table of Contents CycleGAN: Unpaired Image-to-Image Translation (Part 1) Introduction Unpaired Image Translation CycleGAN Pipeline and Training Loss Formulation Adversarial Loss Cycle Consistency Summary Citation Information CycleGAN: Unpaired Image-to-Image Translation (Part 1) In this tutorial, yo... 🏷️ #ComputerVision #CycleGAN #DeepLearning #Keras #KerasandTensorFlow #TensorFlow #UnpairedImageTranslation

✨ An interview with Askat Kuzdeuov, computer vision and deep learning researcher ✨ πŸ“– In this blog post, I interview Askat Ku
✨ An interview with Askat Kuzdeuov, computer vision and deep learning researcher ✨ πŸ“– In this blog post, I interview Askat Kuzdeuov, a computer vision and deep learning researcher at the Institute of Smart Systems and Artificial Intelligence (ISSAI). Askat is not only a stellar researcher, but he’s an avid PyImageSearch reader as well.…... 🏷️ #DeepLearning #GenerativeAdversarialNetworksGANs #Interviews #SensorFusion

✨ An interview with Raul Garcia-Martin, PhD candidate and computer vision entrepreneur ✨ πŸ“– In this blog post, I sit down wit
✨ An interview with Raul Garcia-Martin, PhD candidate and computer vision entrepreneur ✨ πŸ“– In this blog post, I sit down with Raul Garcia-Martin, a PhD candidate in Biometric Recognition at the University Carlos III of Madrid. Raul’s work focuses on identifying individual people by their biometrics. You’re likely already familiar with the most…... 🏷️ #DeepLearning #Interviews #SensorFusion

✨ An interview with David Bonn, computer vision and wildfire detection expert ✨ πŸ“– Imagine this: You’ve built a brand new hom
✨ An interview with David Bonn, computer vision and wildfire detection expert ✨ πŸ“– Imagine this: You’ve built a brand new home out in the country, far from major cities. You need a break from all the hustle and bustle, and you want to bring yourself back to nature. The house you’ve built is…... 🏷️ #DeepLearning #EmbeddedIoTComputerVision #Interviews

✨ An Interview with Peter Ip, Chief Data Scientist ✨ πŸ“– Hey everyone, welcome to another blog post where we talk with student
✨ An Interview with Peter Ip, Chief Data Scientist ✨ πŸ“– Hey everyone, welcome to another blog post where we talk with students from PyImageSearch. Today we are joined by Peter Ip, a Chief Data Scientist. Ritwik: So Peter, maybe you could start by introducing yourself? What do you do, where…... 🏷️ #ChiefDataScientist #DeepLearning #Interviews

✨ Adversarial Learning with Keras and TensorFlow (Part 3): Exploring Adversarial Attacks Using Neural Structured Learning (NS
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✨ Unlocking Image Clarity: A Comprehensive Guide to Super-Resolution Techniques ✨ πŸ“– Table of Contents Unlocking Image Clarit
✨ Unlocking Image Clarity: A Comprehensive Guide to Super-Resolution Techniques ✨ πŸ“– Table of Contents Unlocking Image Clarity: A Comprehensive Guide to Super-Resolution Techniques Introduction Configuring Your Development Environment Need Help Configuring Your Development Environment? What Is Super-Resolution? Usual Problems with Low-Resolution Imagery Traditional Computer Vision A... 🏷️ #ArtificialIntelligence #ComputerVision #DeepLearning #ImageProcessing #MachineLearning #TechnologyApplications #Tutorial

πŸ”₯ Trending Repository: BitNet πŸ“ Description: Official inference framework for 1-bit LLMs πŸ”— Repository URL: https://github.
πŸ”₯ Trending Repository: BitNet πŸ“ Description: Official inference framework for 1-bit LLMs πŸ”— Repository URL: https://github.com/microsoft/BitNet πŸ“– Readme: https://github.com/microsoft/BitNet#readme πŸ“Š Statistics: 🌟 Stars: 20.8K stars πŸ‘€ Watchers: 193 🍴 Forks: 1.6K forks πŸ’» Programming Languages: Python - C++ 🏷️ Related Topics: Not available ================================== 🧠 By: https://t.me/DataScienceM

πŸ”₯ Trending Repository: epicenter πŸ“ Description: Press shortcut β†’ speak β†’ get text. Free and open source. More local-first apps soon ❀️ πŸ”— Repository URL: https://github.com/epicenter-so/epicenter 🌐 Website: https://epicenter.so/ πŸ“– Readme: https://github.com/epicenter-so/epicenter#readme πŸ“Š Statistics: 🌟 Stars: 2.2K stars πŸ‘€ Watchers: 9 🍴 Forks: 131 forks πŸ’» Programming Languages: TypeScript - Svelte - Astro - Rust - CSS - JavaScript - HTML 🏷️ Related Topics:
#svelte #tauri #tailwindcss #sveltekit
================================== 🧠 By: https://t.me/DataScienceM