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:Machine Learning with Python Индия4 808 Образование2 372...

📈 Аналитический обзор Telegram-канала Machine Learning with Python

Канал Machine Learning with Python (@codeprogrammer) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 68 118 подписчиков, занимая 2 372 место в категории Образование и 4 808 место в регионе Индия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 68 118 подписчиков.

Согласно последним данным от 27 августа, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 112, а за последние 24 часа — 8, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 4.52%. В первые 24 часа после публикации контент обычно набирает 1.90% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 3 077 просмотров. В течение первых суток публикация набирает 1 291 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 28 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.

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68 118

Подписчики

+824 часа

-727 дней

+11230 день

3 077

Просмотры поста

~ 1 29124 часа

~ 1 73248 часов

4.52%

Коэффициент вовлеченности

~ 2

Постов в день

Ads index

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Архив постов

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📚 AI / ML / DL Learning Resources Hub 🤖🧠 A structured, end-to-end roadmap to master AI — from fundamentals to cutting-edge research. 🚀 A carefully curated, all-in-one repository designed to help Computer Science students, AI enthusiasts, and professionals 👩‍💻👨‍💻 who want to build strong foundations and progress confidently from beginner to advanced levels 📈. This hub brings together the high-quality books 📖, courses 🎓, playlists 🎵, research papers 📝, tools 🛠, and learning roadmaps 🗺 covering: Artificial Intelligence, Machine Learning, Deep Learning, Data Science 📊, Transformers, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and MLOps 🔄, all organized in a clear, practical, and industry-relevant manner. The resources are selected to balance theory 🧠, intuition 💡, and real-world application 🌍, allowing learners to follow modules sequentially or in parallel ⏳ based on their goals. ⭐️ Recommended resources highlight high-impact content widely used in academia 🏛, research 🔬, and industry 🏭, ensuring you focus on what truly matters in modern AI. 🆘 Repo: https://github.com/bishwaghimire/ai-learning-roadmaps By: https://t.me/CodeProgrammer

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Machine Learning Specialization — Study Notes & Labs Personal notes and lab notebooks from the Machine Learning Specialization by DeepLearning.AI & Stanford Online (Coursera), instructed by Prof. Andrew Ng. 📂 Repo: https://github.com/TruongDat05/machine-learning-notes-and-code

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🔥 Convolutional Neural Networks: Clearly explained! 🖼 Convolutional Neural Networks (CNNs): CNNs belong to the deep learning methods with layers like convolutional, pooling, and fully-connected layers that transform input images for recognition. ➡️ Feedforward Process: Data flows from input to output layers. Images undergo convolution operations, ReLu activation, and Max-Pooling to reduce size and enhance translation and scaling invariance. Finally, data is classified through a fully connected network. 🔄 Training Process: The training involves batches, backpropagation, and gradient descent to minimize errors. The weights start with random values and are updated through backpropagation. This cycle repeats until accuracy is achieved. 📊 Use Cases: CNNs excel in processing images, videos, and audio for tasks like classification, segmentation, and object detection. ⚠️ Limitations: While CNNs handle translation and scaling well, they struggle with rotation invariance. Want to learn more about CNNs? Then, check out super-detailed article about it. 👇 https://lnkd.in/eyA_DnYj

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This Machine Learning Cheat Sheet Saved Me Hours of Revision ⏳ It includes: ✅ Supervised & Unsupervised algorithms ✅ Regression, Classification & Clustering techniques ✅ PCA & Dimensionality Reduction ✅ Neural Networks, CNN, RNN & Transformers ✅ Assumptions, Pros/Cons & Real-world use cases Whether you're: 🔹 Preparing for data science interviews 🔹 Working on ML projects 🔹 Or strengthening your fundamentals this one-page guide is a must-save. ♻️ Repost and share with your ML circle. #MachineLearning #DataScience #AI #MLAlgorithms #InterviewPrep #LearnML

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