👨🏻‍💻 One of the most popular GitHub repositories for "learning and using algorithms in Python" is The Algorithms - Python repo with 196K stars.

✏️ It has a lot of organized and categorized code that you can use to find, read, and run different algorithms. Everything you can think of is here; from simple algorithms like sorting to advanced algorithms for machine learning, artificial intelligence, neural networks, and more.

✅ Why should we use it?

🔢 For learning: If you're looking to learn algorithms in action, this is great.

🔢 For practice: You can take the codes, run them, and modify them to better understand.

🔢 For projects : You can even use the codes here in real-life or academic projects.

🔢 For interviews: If you're preparing for data science interviews, this is full of practical algorithms.


┌ 🏳️‍🌈 The Algorithms - Python
└ 🐱 GitHub-Repos

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15 ways to optimize neural network training

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GANs clearly explained with visuals

This website provides a clear explanation, Try it out yourself: poloclub.github.io/ganlab/

📂 Tags: #DataScience #Python #ML #AI #LLM #Courses #Pandas #DV #GAN

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🪙 Everything you need to get started in machine learning.

Start with Python, explore scikit-learn, and neural networks using PyTorch. Perfect for beginners—get the skills you need to advance your career in just a few hours.

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Arcade Academy - Learn Python 🖥

📖 Book

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pandas Project: Make a Gradebook With Python & pandas

Link: https://realpython.com/pandas-project-gradebook/

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A visual deep dive into Transformers (LLMs)

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Machine Learning Algorithm basics!
Support Vector Machines (SVM) vs k-Nearest Neighbors (k-NN)

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Python password generator 🖥

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Fetch Trending Searches using Python 🖥

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Best Data Analyst Online Certifications!
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#Google #Colab notebooks to learn #Transformers 😎

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Top 100 Machine Learning Interview Questions & Answers Cheatsheet

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Machine Learning from Scratch by Danny Friedman

This book is for readers looking to learn new machine learning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different algorithms create the models they do and the advantages and disadvantages of each one.

This book will be most helpful for those with practice in basic modeling. It does not review best practices—such as feature engineering or balancing response variables—or discuss in depth when certain models are more appropriate than others. Instead, it focuses on the elements of those models.

🌟 Link: https://dafriedman97.github.io/mlbook/content/introduction.html

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