Data Science Machine Learning Data Analysis
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This channel is for Programmers, Coders, Software Engineers.

1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning
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πŸ“š 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

◀️ A strong start in the world of neural networks, CNNs and RNNs.

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1️⃣ Linear Algebra with 3Blue1Brown

◀️ Intuitive and visual teaching of linear algebra concepts.

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πŸ”΄ 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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πŸš€ Master the Transformer Architecture with PyTorch! 🧠

Dive deep into the world of Transformers with this comprehensive PyTorch implementation guide. Whether you're a seasoned ML engineer or just starting out, this resource breaks down the complexities of the Transformer model, inspired by the groundbreaking paper "Attention Is All You Need".

πŸ”— Check it out here:
https://www.k-a.in/pyt-transformer.html

This guide offers:

🌟 Detailed explanations of each component of the Transformer architecture.

🌟 Step-by-step code implementations in PyTorch.

🌟 Insights into the self-attention mechanism and positional encoding.

By following along, you'll gain a solid understanding of how Transformers work and how to implement them from scratch.

#MachineLearning #DeepLearning #PyTorch #Transformer #AI #NLP #AttentionIsAllYouNeed #Coding #DataScience #NeuralNetworks
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πŸ”΄ Comprehensive course on "Data Mining"
πŸ–₯ Carnegie Mellon University, USA


πŸ‘¨πŸ»β€πŸ’» Carnegie University in the United States has come to offer a free #datamining course in 25 lectures to those interested in this field.

◀️ In this course, you will deal with statistical concepts and model selection methods on the one hand, and on the other hand, you will have to implement these concepts in practice and present the results.

◀️ The exercises are both combined: theory, #coding, and practical.πŸ‘‡


β”Œ πŸ₯΅ Data Mining
└⏯️ Course Homepage

πŸ’― BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
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πŸ”₯ Trending Repository: awesome-claude-code

πŸ“ Description: A curated list of awesome commands, files, and workflows for Claude Code

πŸ”— Repository URL: https://github.com/hesreallyhim/awesome-claude-code

πŸ“– Readme: https://github.com/hesreallyhim/awesome-claude-code#readme

πŸ“Š Statistics:
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πŸ‘€ Watchers: 96
🍴 Forks: 606 forks

πŸ’» Programming Languages: Python - Makefile - Shell

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🧠 By: https://t.me/DataScienceM
πŸ“Œ Make Python Up to 150Γ— Faster with C

πŸ—‚ Category: PROGRAMMING

πŸ•’ Date: 2025-11-10 | ⏱️ Read time: 14 min read

Dramatically accelerate your Python applicationsβ€”up to 150x fasterβ€”by strategically offloading performance-critical code to C. This practical guide shows how to seamlessly integrate C with your existing Python projects, supercharging your code's bottlenecks without abandoning the Python ecosystem. Achieve significant performance gains where they matter most.

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