250 Coursera FREE Courses [Data Science, Machine Learning, Python] 2024 🏵

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Pandas Data Cleaning (Guide)

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Pandas Data Cleaning (Guide)

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Confusion matrix (TP, FP, TN, FN), clearly explained

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📝 Cheat sheets for data science and machine learning

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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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Mathematical theory of Deep Learning:

[Download 282-page PDF. Updated version]:
arxiv.org/abs/2407.18384

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Gradio: The easiest way to demo your models.

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- Works with any Python code, including custom functions.

- Share via temporary links or deploy on #HuggingFace Spaces.

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If you're looking to create interactive demos for your ML project, check out #Gradio!

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😀 Introduction to Machine Learning – Laurent Younes

Looking for a clear and concise introduction to machine learning? This book by Laurent Younes provides a solid foundation in ML concepts, from theory to practical applications.

Perfect for students, researchers, and enthusiasts aiming to build a strong understanding of the core principles behind modern machine learning.

📄 Read the Book (PDF)
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