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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Сітка:📈 Аналітичний огляд Telegram-каналу Machine Learning with Python
Канал Machine Learning with Python (@codeprogrammer) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 68 151 підписників, посідаючи 2 379 місце в категорії Освіта та 4 752 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 68 151 підписників.
За останніми даними від 01 вересня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 84, а за останні 24 години на 7, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 4.17%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.54% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 2 845 переглядів. Протягом першої доби публікація в середньому набирає 1 052 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 5.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як insidead, learning, degree, evaluation, algorithm.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Завдяки високій частоті оновлень (останні дані отримано 02 вересня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Освіта.
68 151
Підписники
+724 години
+247 днів
+8430 днів
Архів дописів
A Complete Course to Learn Robotics and Perception
Notebook-based book "Introduction to Robotics and Perception" by Frank Dellaert and Seth Hutchinson
github.com/gtbook/robotics
roboticsbook.org/intro.html
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📂 8 Steps to Mastering MLOps
✅ For data scientists
⏯️ Introduction to MLOps
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2️⃣ Model Management
📎 ML Model Registry
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3️⃣ Building a pipeline of models
📎 Building End-to-End ML Pipelines
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📎 Orchestration & ML Pipelines
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4️⃣ Monitoring models
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5️⃣ Introduction to Docker
📎 Docker Tutorial
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6️⃣ Designing ML systems
📎 Designing ML Systems
📎 ML System Design Patterns
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7️⃣ Sample projects
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8️⃣ Comprehensive roadmap
📎 MLOps Roadmap 2024
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Open Guide to Data Structures and Algorithms
A must-read for anyone starting their journey in computer science and programming. This open-access book offers a clear, beginner-friendly introduction to the core concepts of data structures and algorithms, with simple explanations and practical examples. Whether you're a student or a self-learner, this guide is a solid foundation to build your DSA knowledge. Highly recommended for those who want to learn efficiently and effectively.
Read it here:
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Datasets Guide 📚
A practical and beginner-friendly guide that walks you through everything you need to know about datasets in machine learning and deep learning. This guide explains how to load, preprocess, and use datasets effectively for training models. It's an essential resource for anyone working with LLMs or custom training workflows, especially with tools like Unsloth.
Importance:
Understanding how to properly handle datasets is a critical step in building accurate and efficient AI models. This guide simplifies the process, helping you avoid common pitfalls and optimize your data pipeline for better performance.
Link: https://docs.unsloth.ai/basics/datasets-guide
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Master PyTorch Faster with These Free Resources!
Whether you're just getting started with PyTorch or looking to refresh your deep learning skills, these two resources are all you need:
1. PyTorch Cheatsheet
A concise reference guide packed with essential PyTorch commands and patterns. Perfect for quick look-ups during development.
Download:
https://www.dropbox.com/scl/fi/e4xngykrfoubiw3xnd6fz/PyTorch-Cheatsheet.pdf?rlkey=vgx38ckps7aie120imgozgq4g&e=2&st=hgs06d4t&dl=0
2. Learn PyTorch Deep Learning with Hands-On Code
A beginner-friendly PDF with practical examples to help you build and train deep learning models using PyTorch from scratch.
Download:
https://www.dropbox.com/scl/fi/lfo7r6fnd8wjm3gp0jteh/Learn-PyTorch-Deep-Learning-with-Hands-On-Code.pdf?rlkey=mg9cxg41yerouzp0rklm8hqa2&e=2&st=c7k7rgay&dl=0
Save them, share them, and start building smarter models today!
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Foundations of Large Language Models
Download it: https://readwise-assets.s3.amazonaws.com/media/wisereads/articles/foundations-of-large-language-/2501.09223v1.pdf
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💧 18 of the best blogs to start and grow on your data science path!
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💬 The path to growth in data has nothing to do with your starting point, what matters is how consistently you learn.
✅ These blogs are like a mentor in your email; every week a new insight, a learning, a new idea. 👇
🥵 The Data Hustle Blog
⏪ Real experiences from the world of data in a friendly tone.
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🥵 Diving Into Data Blog
⏪ Summarizing data-heavy concepts in simple language.
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🥵 Tech Growth Series Blog
⏪ A guide to professional growth in the field of technology and data.
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🥵 Data Neighbor Blog
⏪ Discussion and experience of the real learning path in the data field.
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🥵 DataEngineer.io Blog
⏪ The world of data engineering with real solutions.
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🥵 The Data Analyst Blueprint Blog
⏪ A complete roadmap to becoming a data analyst step by step.
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🥵 Jam with AI Blog
⏪ Up-to-date content in the field of AI; suitable for starting and continuing learning.
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🥵 Zero2Dataengineer Blog
⏪ From zero to becoming an engineer with a specific path.
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🥵 Maistermind Blog
⏪ Deep insights and systems thinking in data.
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⏪ Data science + healthy lifestyle = true balance.
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⏪ Continuous learning and golden tips for data scientists.
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⏪ An excellent compilation of useful resources and tools in the world of data.
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⏪ Exploring neural models and artificial intelligence with tangible examples.
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⏪ Data, personal growth, and new paths for the future of work.
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📖 100 Essential Data Science Interview Questions
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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
📚 Become a professional data scientist with these 17 resources!
1️⃣ Python libraries for machine learning
◀️ Introducing the best Python tools and packages for building ML models.
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2️⃣ Deep Learning Interactive Book
◀️ Learn deep learning concepts by combining text, math, code, and images.
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3️⃣ Anthology of Data Science Learning Resources
◀️ The best courses, books, and tools for learning data science.
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4️⃣ Implementing algorithms from scratch
◀️ Coding popular ML algorithms from scratch
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5️⃣ Machine Learning Interview Guide
◀️ Fully prepared for job interviews
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6️⃣ Real-world machine learning projects
◀️ Learning how to build and deploy models.
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7️⃣ Designing machine learning systems
◀️ How to design a scalable and stable ML system.
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8️⃣ Machine Learning Mathematics
◀️ Basic mathematical concepts necessary to understand machine learning.
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9️⃣ Introduction to Statistical Learning
◀️ Learn algorithms with practical examples.
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1️⃣ Machine learning with a probabilistic approach
◀️ Better understanding modeling and uncertainty with a statistical perspective.
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1️⃣ UBC Machine Learning
◀️ Deep understanding of machine learning concepts with conceptual teaching from one of the leading professors in the field of ML,
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1️⃣ Deep Learning with Andrew Ng
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1️⃣ Linear Algebra with 3Blue1Brown
◀️ Intuitive and visual teaching of linear algebra concepts.
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🔴 Machine Learning Course
◀️ A combination of theory and practical training to strengthen ML skills.
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1️⃣ Mathematical Optimization with Python
◀️ You will learn the basic concepts of optimization with Python code.
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1️⃣ Explainable models in machine learning
◀️ Making complex models understandable.
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⚫️ Data Analysis with Python
◀️ Data analysis skills using Pandas and NumPy libraries.
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