#Google #Colab notebooks to learn #Transformers 😎

Link: https://github.com/nlp-with-transformers/notebooks

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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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https://t.iss.one/CodeProgrammer ✅

Gradio: The easiest way to demo your models.

- Core Idea: Quickly turn #ML models into interactive web apps.

- No frontend skills needed. It's all #Python.

- Works with any Python code, including custom functions.

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

- Get user feedback to improve your models.

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

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⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟

🥇 This repo is like gold for every data scientist!

✅ Just open your browser; a ton of interactive exercises and real experiences await you. Any question about statistics, probability, Python, or machine learning, you'll get the answer right there! With code, charts, even animations. This way, you don't waste time, and what you learn really sticks in your mind!

⬅️ Data science statistics and probability topics
⬅️ Clustering
⬅️ Principal Component Analysis (PCA)
⬅️ Bagging and Boosting techniques
⬅️ Linear regression
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┌ 📂 Int Data Science Python Dash
└ 🐱 GitHub-Repos

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