Data Science Machine Learning Data Analysis

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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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Data Science Machine Learning Data Analysis

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🔖 The book that paved the way for me to "data science"!

👨🏻‍💻 "Where do I start now?" This was the first and biggest question I faced when I started my Data Science learning journey!

⏪ I was really overwhelmed by the large number of scattered sources, long courses, and specialized books full of heavy terminology. I didn't know how to start and move forward in this direction...

✔️ But the book Intro to Data Science with Python changed everything for me and gave me a new perspective!

✏️ This book is a complete guide to starting from scratch and is great for both beginners and professionals in this field!! From coding with Python to working with data, visualization, and even AI tools, it explains everything in the simplest and most practical way possible.

💸 A great start for anyone looking to learn data science with Python!👇

┌ 🏳️‍🌈 Intro to Data Science with Python
├ 📄 E-book
└ 🐱 GitHub-Repos

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10 Must-Know Python Libraries for LLMs in 2025

Large language models (LLMs) are changing the way we think about AI. They help with #chatbots, text generation, and search tools, among other natural language processing tasks and beyond. To work with #LLMs, you need the right #Python libraries.

In this article, we explore 10 of the Python libraries every developer should know in 2025.

Read and learn:
https://machinelearningmastery.com/10-must-know-python-libraries-for-llms-in-2025/

https://t.iss.one/CodeProgrammer

✅

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Data Science Machine Learning Data Analysis

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course lecture on building Transformers from first principles:
https://www.dropbox.com/scl/fi/jhfgy8dnnvy5qq385tnms/lectureattentionneuralnetworks.pdf?rlkey=fddnkonsez76mf8bzider3hrv&dl=0

The #PyTorch notebooks also demonstrate how to implement #Transformers from scratch:
https://github.com/xbresson/CS52422025/tree/main/labslecture07

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Data Science Machine Learning Data Analysis

The Beginner’s Guide to Machine Learning with Rust

In this article, we’ll explore the fundamentals of #machinelearning in #Rust, walk through essential libraries, and build a simple machine learning model. Whether you’re a Rust developer curious about machine learning or an machine learning practitioner looking for a high-performance alternative, this article will help you get started.

Link: https://machinelearningmastery.com/the-beginners-guide-to-machine-learning-with-rust/

https://t.iss.one/DataScienceM ❤️

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Data Science Machine Learning Data Analysis

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Pandas Introduction to Advanced.pdf

854.8 KB

📄

"Pandas Introduction to Advanced" booklet

👨🏻‍💻 You can't attend a #datascience interview and not be asked about Pandas! But you don't have to memorize all its methods and functions! With this booklet, you'll learn everything you need.

✔️ One of the most useful and interesting combinations is using #Pandas with #AWS Lambda, which can be very useful in real projects.

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Data Science Machine Learning Data Analysis

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Data Science Machine Learning Data Analysis

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Exercises in Machine Learning

This book contains 75+ exercises

Download, read, and practice:
arxiv.org/pdf/2206.13446

GitHub Repo: https://github.com/michaelgutmann/ml-pen-and-paper-exercises

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Data Science Machine Learning Data Analysis

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Linear Algebra

The 2nd best book on linear algebra with ~1000 practice problems. A MUST for AI & Machine Learning.

Completely FREE.

Download it: https://www.cs.ox.ac.uk/files/12921/book.pdf

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Data Science Machine Learning Data Analysis

#MachineLearning Systems — Principles and Practices of Engineering Artificially Intelligent Systems: https://mlsysbook.ai/

open-source textbook focuses on how to design and implement AI systems effectively

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Data Science Machine Learning Data Analysis

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Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.

https://t.iss.one/DataScienceN

Data Science Jupyter Notebooks

Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.

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Data Science Machine Learning Data Analysis

Forwarded from Python | Machine Learning | Coding | R

🔗 Machine Learning from Scratch by Danny Friedman

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

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