📚 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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🔴 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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🚀 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".

🔗 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



💯 BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟

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