Machine Learning
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Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho
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Сеть:📈 Аналитический обзор Telegram-канала Machine Learning
Канал Machine Learning (@machinelearning9) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 41 668 подписчиков, занимая 3 145 место в категории Технологии и приложения и 215 место в регионе Сирия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 41 668 подписчиков.
Согласно последним данным от 05 октября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 533, а за последние 24 часа — 13, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 6.25%. В первые 24 часа после публикации контент обычно набирает 1.92% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 2 601 просмотров. В течение первых суток публикация набирает 800 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 6.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как distance, insidead, gpu, learning, degree.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Real Machine Learning — simple, practical, and built on experience.
Learn step by step with clear explanations and working code.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Благодаря высокой частоте обновлений (последние данные получены 06 октября, 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 Технологии и приложения.
41 668
Подписчики
+1324 часа
+1357 дней
+53330 дней
Архив постов
41 668
Repost from Machine Learning with Python
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41 668
"Learning Mathematics: Why Memory, Practice, and Technique are More Important than Talent"
To master mathematics, it is necessary to memorize a large amount of information, rules, methods of simplification, and problem-solving techniques. There is no other way. Theory is useful, but in moderation.
It's like learning a language. If you focus too much on grammar, you will never learn to speak it fluently.
https://algebrica.org/learning-mathematics/
41 668
Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/Codeprogrammer
41 668
Repost from Machine Learning with Python
+1
"How to Train a Neural Network" is a concise summary of the MIT course lectures on deep learning from 2024. It focuses on one of the fundamental questions in neural networks: how a model learns its weights.
The summary examines the training process from a mathematical perspective. It covers topics such as forward propagation, loss functions, gradients, backpropagation, and gradient-based optimization methods.
I believe this is an interesting resource for those who want to go beyond a general, intuitive understanding of neural networks and begin to delve into the mathematics that underlies their training.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/mit6_7960_f24_lec2.pdf
https://t.me/CodeProgrammer 🤩
41 668
Repost from Machine Learning with Python
If you're just starting to learn machine learning and want to delve deeper into the mathematics required for machine learning and deep learning, I recommend trying this platform. It's something like LeetCode for machine learning.
This is not an advertisement: I personally used it and decided to share it with you.
https://deep-ml.com
https://t.me/CodeProgrammer
41 668
Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/Codeprogrammer
41 668
🤖 A Practical Tip for ML Data Collection
When building a machine learning project, getting enough useful data is often just as important as the model itself.
If you're collecting public web data for a dataset, you may need to access the same source from different locations or test how location affects the data returned.
A residential proxy can help with this by routing your requests through IPs from different regions.
For example, with Python:
import requests
proxies = {
"http": "http://USER:PASSWORD@HOST:PORT",
"https": "http://USER:PASSWORD@HOST:PORT"
}
response = requests.get(
"https://example.com",
proxies=proxies
)
print(response.status_code)