Machine Learning with Python

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.me/CodeProgrammer ✅

🔥 Transformers Laid Out 📌 Guide

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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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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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