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Discover powerful insights with Python, Machine Learning, Coding, and R—your essential toolkit for data-driven solutions, smart alg

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📚 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.



2️⃣ Deep Learning Interactive Book

◀️ Learn deep learning concepts by combining text, math, code, and images.



3️⃣ Anthology of Data Science Learning Resources

◀️ The best courses, books, and tools for learning data science.



4️⃣ Implementing algorithms from scratch

◀️ Coding popular ML algorithms from scratch



5️⃣ Machine Learning Interview Guide

◀️ Fully prepared for job interviews



6️⃣ Real-world machine learning projects

◀️ Learning how to build and deploy models.



7️⃣ Designing machine learning systems

◀️ How to design a scalable and stable ML system.



8️⃣ Machine Learning Mathematics

◀️ Basic mathematical concepts necessary to understand machine learning.



9️⃣ Introduction to Statistical Learning

◀️ Learn algorithms with practical examples.



1️⃣ Machine learning with a probabilistic approach

◀️ Better understanding modeling and uncertainty with a statistical perspective.



1️⃣ UBC Machine Learning

◀️ Deep understanding of machine learning concepts with conceptual teaching from one of the leading professors in the field of ML,



1️⃣ Deep Learning with Andrew Ng

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



1️⃣ Linear Algebra with 3Blue1Brown

◀️ Intuitive and visual teaching of linear algebra concepts.



🔴 Machine Learning Course

◀️ A combination of theory and practical training to strengthen ML skills.



1️⃣ Mathematical Optimization with Python

◀️ You will learn the basic concepts of optimization with Python code.



1️⃣ Explainable models in machine learning

◀️ Making complex models understandable.



⚫️ Data Analysis with Python

◀️ Data analysis skills using Pandas and NumPy libraries.


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@Codeprogrammer Cheat Sheet Numpy.pdf
213.7 KB
This checklist covers the essentials of NumPy in one place, helping you:

- Create and initialize arrays
- Perform element-wise computations
- Stack and split arrays
- Apply linear algebra functions
- Efficiently index, slice, and manipulate arrays

…and much more!

Feel free to share if you found this useful, and let me know in the comments if I missed anything!

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Polars.pdf
391.5 KB
📖 A comprehensive cheat sheet for working with Polars


🌟 Have you ever worked with pandas and thought that was the fastest way? I thought the same thing until I worked with Polars.

✏️ This cheat sheet explains everything about Polars in a concise and simple way. Not just theory! But also a bunch of real examples, practical experience, and projects that will really help you in the real world.

🐻‍❄️ Polars Cheat Sheet
♾️ Google Colab
📖 Doc

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