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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Сеть:📈 Аналитический обзор Telegram-канала Machine Learning with Python
Канал Machine Learning with Python (@codeprogrammer) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 68 138 подписчиков, занимая 2 366 место в категории Образование и 4 740 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 68 138 подписчиков.
Согласно последним данным от 30 августа, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 86, а за последние 24 часа — 27, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 4.07%. В первые 24 часа после публикации контент обычно набирает 1.52% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 2 775 просмотров. В течение первых суток публикация набирает 1 037 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 5.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как insidead, learning, degree, evaluation, algorithm.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Благодаря высокой частоте обновлений (последние данные получены 31 августа, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.
68 138
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-297 дней
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Repost from Machine Learning
📌 PyTorch Explained: From Automatic Differentiation to Training Custom Neural Networks
🗂 Category: DEEP LEARNING
🕒 Date: 2025-09-24 | ⏱️ Read time: 15 min read
Deep learning is shaping our world as we speak. In fact, it has been slowly…
Most of the time, you regret helping others in general, because not everyone appreciates your hard work or the effort it takes you to spread a piece of information, even if it's quoted
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Repost from Machine Learning
📌 Paper Walkthrough: Attention Is All You Need
🗂 Category: DEEP LEARNING
🕒 Date: 2024-11-03 | ⏱️ Read time: 46 min read
The complete guide to implementing a Transformer from scratch
Repost from Machine Learning
📌 Building a Convolutional Neural Network (CNNs) from Scratch
🗂 Category:
🕒 Date: 2024-11-05 | ⏱️ Read time: 15 min read
Line-by-Line, Let’s Build a ResNet Classifier on the MNIST-Fashion Dataset
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💯 Use Kaggle like a pro with this method!
👨🏻💻 Never underestimate Kaggle! One of the best ways to start learning data science and ML is Kaggle. A place where theory turns into practice, beginners become professionals, and skills turn into value.
🎯 This roadmap is the key to practical use of this amazing platform:👇
⬅️ Step one: Strengthen your basic skills!
✏️ Start with Kaggle's short and free courses. Practical, focused, and suitable for beginners.
✅ Python ⬅️ Link
☑️ Introduction to Machine Learning ⬅️ Link
✔️ Introduction to Deep Learning ⬅️ Link
✔️ Introduction to SQL ⬅️ Link
✔️ Introduction to Game AI and RL ⬅️ Link
📝 Complete list of courses ⬅️Link
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✅ Classification problem for beginners
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✔️ Fake news detection with NLP
✔️ Deep learning on image data with TPU
📝 Complete list of competitions ⬅️Link
https://t.me/CodeProgrammer 🌟
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👨🏻💻 One of the most popular GitHub repositories for "learning and using algorithms in Python" is The Algorithms - Python repo with 196K stars.
✏️ It has a lot of organized and categorized code that you can use to find, read, and run different algorithms. Everything you can think of is here; from simple algorithms like sorting to advanced algorithms for machine learning, artificial intelligence, neural networks, and more.
✅ Why should we use it?
🔢 For learning: If you're looking to learn algorithms in action, this is great.
🔢 For practice: You can take the codes, run them, and modify them to better understand.
🔢 For projects : You can even use the codes here in real-life or academic projects.
🔢 For interviews: If you're preparing for data science interviews, this is full of practical algorithms.
┌ 🏳️🌈 The Algorithms - Python
└ 🐱 GitHub-Repos
