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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
Больше📈 Аналитический обзор Telegram-канала Machine Learning with Python
Канал Machine Learning with Python (@codeprogrammer) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 67 810 подписчиков, занимая 2 427 место в категории Образование и 5 028 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 67 810 подписчиков.
Согласно последним данным от 13 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 60, а за последние 24 часа — -3, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 4.31%. В первые 24 часа после публикации контент обычно набирает 1.69% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 2 926 просмотров. В течение первых суток публикация набирает 1 148 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 6.
- 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 14 июня, 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.
67 810
Подписчики
-324 часа
-77 дней
+6030 день
Архив постов
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🔺 Data science learning roadmap in 2024
👨🏻💻 If you want to start learning data science from scratch in 2024, this roadmap can be a great starting point for you.👌🏼
1️⃣ Basics of data science
🏷 Statistics and probability
◾┘️ link: Statistics & Probability
🏷 Linear Algebra
┘ ◽️ link: Essence of Linear Algebra
2️⃣ Programming language
🏷 Python
◾┘️ link: Learn Python 3
3️⃣ Data analysis and manipulation
🏷 Pandas library
◾┘️ link: pandas documentation
🏷 Data preparation with Pandas
Link : Data Wrangling with Pandas
🏷 NumPy library
◾┘️ link: NumPy documentation
4️⃣ Data visualization
🏷 Matplotlib and Seaborn library
◾┘️ Link: Matplotlib / seaborn
🏷 Tableau Public platform
Link : Tableau Public
5️⃣ Principles of machine learning
🏷 scikit-learn library
◾┘️ link: scikit-learn
6️⃣ Learning algorithms
🏷 Hands-On Machine Learning book
◾┘️ link: Hands-On ML
7️⃣ Deep learning
🏷 TensorFlow library
◾┘️ link: TensorFlow Tutorial
🏷 PyTorch library
◾┘️ Link: PyTorch Documentation
8️⃣ Big data technologies
🏷 Spark framework course
◾┘️ Link: Spark Course
9️⃣ Advanced topics
🏷 Natural language processing course in Python
◾┘️ link: NLP in Python
1️⃣ Share your projects on Kaggle and GitHub
🏷 Kaggle platform
◾┘️ link: Kaggle
🏷 GitHub platform
◾┘️ Link: GitHub
⭐️ http://t.me/codeprogrammer
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Understand the fundamental concepts and techniques of web scraping and its legal and ethical considerations.
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Discover how Gen AI revolutionizes the web scraping landscape with real-world examples.
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Get acquainted with the Jina Reader API, a powerful tool for obtaining LLM-friendly input from URLs or web searches.
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Dive into ScrapeGraphAI, a groundbreaking Python library that combines LLMs and direct graph logic for creating robust scraping pipelines.
Event Details:
🗓 Date: 22 June, Saturday
⏰ Time: 11:00 AM IST
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Connect with Founder from IIT Delhi;
https://www.linkedin.com/in/satvik-paramkusham/
🔺 Data science learning roadmap in 2024
👨🏻💻 If you want to start learning data science from scratch in 2024, this roadmap can be a great starting point for you.👌🏼
1️⃣ Basics of data science
🏷 Statistics and probability
◾┘️ link: Statistics & Probability
🏷 Linear Algebra
┘ ◽️ link: Essence of Linear Algebra
2️⃣ Programming language
🏷 Python
◾┘️ link: Learn Python 3
3️⃣ Data analysis and manipulation
🏷 Pandas library
◾┘️ link: pandas documentation
🏷 Data preparation with Pandas
Link : Data Wrangling with Pandas
🏷 NumPy library
◾┘️ link: NumPy documentation
4️⃣ Data visualization
🏷 Matplotlib and Seaborn library
◾┘️ Link: Matplotlib / seaborn
🏷 Tableau Public platform
Link : Tableau Public
5️⃣ Principles of machine learning
🏷 scikit-learn library
◾┘️ link: scikit-learn
6️⃣ Learning algorithms
🏷 Hands-On Machine Learning book
◾┘️ link: Hands-On ML
7️⃣ Deep learning
🏷 TensorFlow library
◾┘️ link: TensorFlow Tutorial
🏷 PyTorch library
◾┘️ Link: PyTorch Documentation
8️⃣ Big data technologies
🏷 Spark framework course
◾┘️ Link: Spark Course
9️⃣ Advanced topics
🏷 Natural language processing course in Python
◾┘️ link: NLP in Python
1️⃣ Share your projects on Kaggle and GitHub
🏷 Kaggle platform
◾┘️ link: Kaggle
🏷 GitHub platform
◾┘️ Link: GitHub
🔺 Data science learning roadmap in 2024
✅ If you want to start learning data science from scratch in 2024, this roadmap can be a great starting point for you.
1️⃣ Basics of data science
🏷 Statistics and probability
◾️┘️ link: Statistics & Probability
🏷 Linear Algebra
◾️┘️ link: Essence of Linear Algebra
2️⃣ Programming language
🏷 Python
◾️┘️ link: Learn Python 3
3️⃣ Data analysis and manipulation
🏷 Pandas library
◾️┘️ link: pandas documentation
🏷 Data preparation with Pandas
◾️┘️ Link : Data Wrangling with Pandas
🏷 NumPy library
◾️┘️ link: NumPy documentation
4️⃣ Data visualization
🏷 Matplotlib and Seaborn library
◾️┘️ Link: Matplotlib / seaborn
🏷 Tableau Public platform
◾️┘️ Link : Tableau Public
5️⃣ Principles of machine learning
🏷 scikit-learn library
◾️┘️ link: scikit-learn
6️⃣ Learning algorithms
🏷 Hands-On Machine Learning book
◾️┘️ link: Hands-On ML
7️⃣ Deep learning
🏷 TensorFlow library
◾️┘️ link: TensorFlow Tutorial
🏷 PyTorch library
◾️┘️ Link: PyTorch Documentation
8️⃣ Big data technologies
🏷 Spark framework course
◾️┘️ Link: Spark Course
9️⃣ Advanced topics
🏷 Natural language processing course in Python
◾️┘️ link: NLP in Python
1️⃣ Share your projects on Kaggle and GitHub
🏷 Kaggle platform
◾️┘️ link: Kaggle
🏷 GitHub platform
◾️┘️ Link: GitHub
🔺 10 free MIT data science courses
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✅ Here I have put the top 10 free MIT data science courses for you in 2024. 👇
🏷 MIT University's free data science courses
┤ ✅ Computational thinking and data science introductory course
┤ ✅ machine learning course with Python
┤ ✅ Computer science and programming course with Python
┤ ✅ Supply chain analysis course
┤ ✅ Understanding the world through data course
┤ ✅ Computational thinking course for modeling and simulation
┤ ✅ Probability Course - Science of Uncertainty and Data
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┤ ✅ Principles and basics of statistics and probability course
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🔺 10 free MIT data science courses
☄️ If you have started learning data science, improve your learning level right now with the courses of prestigious universities and institutions in the world such as Stanford, Harvard and MIT, which are the first in the field of data science.
✅ Here I have put the top 10 free MIT data science courses for you in 2024. 👇
🏷 MIT University's free data science courses
┤ ◼️ Computational thinking and data science introductory course
┤ ◻️ machine learning course with Python
┤ ◼️ Computer science and programming course with Python
┤ ◻️ Supply chain analysis course
┤ ◼️ Understanding the world through data course
┤ ◻️ Computational thinking course for modeling and simulation
┤ ◼️ Probability Course - Science of Uncertainty and Data
┤ ◻️ The course of principles of production processes
┤ ◼️ Principles and basics of statistics and probability course
┘ ◻️ The course of becoming an entrepreneur
