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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Red:📈 Análisis del canal de Telegram Machine Learning with Python
El canal Machine Learning with Python (@codeprogrammer) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 68 109 suscriptores, ocupando la posición 2 375 en la categoría Educación y el puesto 4 809 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 68 109 suscriptores.
Según los últimos datos del 26 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 120, y en las últimas 24 horas de -14, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 4.55%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.02% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 3 099 visualizaciones. En el primer día suele acumular 1 378 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 5.
- Intereses temáticos: El contenido se centra en temas clave como insidead, learning, degree, evaluation, algorithm.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 27 agosto, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.
68 109
Suscriptores
-1424 horas
-667 días
+12030 días
Archivo de publicaciones
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Repost from Data Science Books
📕 Large Language Models 2026
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Repost from Machine Learning
Classical machine learning equations and diagrams cheat sheet 📊
https://github.com/soulmachine/machine-learning-cheat-sheet
#MachineLearning #ML #DataScience #CheatSheet #AI #DeepLearning
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The guide Path to Senior Engineer Handbook has gathered resources for developers who want to advance to the level of Senior Engineer. 🚀
Inside: 📚
More than 50 newsletters on professional growth, system design, leadership, and web development. 📈
A selection of books on communication, technical writing, and building working relationships. 🤝
Selected YouTube channels, podcasts, and professional communities. 🎧
Courses, scientific articles, and educational platforms for a deeper study of topics. 🎓
A good starting point for those who want to improve not only their technical skills, but also their architectural thinking, communication, and leadership competencies. 💡
Link: https://github.com/jordan-cutler/path-to-senior-engineer-handbook?utm_source=opensourceprojects.dev&ref=opensourceprojects.dev
#SeniorEngineer #CareerGrowth #SoftwareEngineering #TechLeadership #SystemDesign #DevCommunity
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
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Want any LLM to answer from your own documents?
Most RAG setups quietly give weak, vague answers, and the model is almost never the real problem. Three small fixes decide whether it works, and the exact tools to use in 2026 are very specific.
Full, concrete guide in one post.
On Reddit, someone collected all the resources they used to prepare for interviews on algorithms, system design, and machine coding. In the end, they made it to a Google interview.
1. Algorithms and Patterns
Before grinding on problems, it's worth understanding the patterns.
• All LeetCode Articles on Coding Patterns Summarized
https://leetcode.com/discuss/interview-question/5366542/all-leetcode-articles-on-coding-patterns-summarized-in-one-page
• Solved All Two Pointers Problems in 100 Days
https://leetcode.com/discuss/study-guide/1688903/Solved-all-two-pointers-problems-in-100-days
• Tree Question Pattern 2023 — Tree Study Guide
https://leetcode.com/discuss/study-guide/2879240/tree-question-pattern-2023-tree-study-guide
• Important and Useful Links from All Over LeetCode
https://leetcode.com/discuss/general-discussion/665604/Important-and-Useful-links-from-all-over-the-LeetCode
• Coding Interview Preparation Problems for Beginners
https://leetcode.com/discuss/interview-question/448284/Coding-Interview-preparation-problems-for-beginners
2. Preparation for Companies
• Google, Meta, Apple, Amazon Senior SDE Preparation
https://prachub.com/?sort=hot&company=Meta%2CGoogle%2CTikTok%2CAmazon
• A Study Guide for Passing the Google Interview
https://prachub.com/interview-guide
The author also made a small tracker for preparation:
• company-specific questions
• Todo / Solved / Revision statuses
• automatic repetition scheduling
• AI assistant with hints instead of ready-made solutions
https://prachub.com/questions
3. System Design (HLD)
Instead of random articles — structured collections:
• Arch 25 — the most common systems and patterns
• Arch 50 — infrastructure, data, and fault tolerance
• Arch 75 — more complex scenarios and company-specific specialization
• Arch All — a full bank of 103 HLD tasks
• Core Concepts — 33 breakdowns of distributed systems
4. Machine Coding / LLD
Many underestimate this part until their first interview failure.
• MaCo 30 — the most common tasks
• MaCo 60 — an extended collection
• MaCo All — a full set of 103 tasks
• Design Patterns — 31 design patterns
Repost from Data Analytics
🔖 Cheat sheets on Transformers and LLMs from Stanford
We found an excellent set of materials from the Stanford CME-295 course on large language models.
They cover tokenization, the self-attention mechanism, prompting, fine-tuning, LLM-as-a-judge, RAG, AI agents, and reasoning models.
⛓️ Download here
https://github.com/afshinea/stanford-cme-295-transformers-large-language-models
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