Machine Learning with Python

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"Data Structures & Algorithms using Python" This book of 222 pages implements all types of #DATASTRUCTURES and #ALGORITHMS. And it's 💯 #FREE. Download Free: https://donsheehy.github.io/datastructures/fullbook.pdf By: https://t.me/DataScience4

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Stanford’s Machine Learning - by Andrew Ng A complete lecture notes of 227 pages. Available Free. Download the notes: cs229.stanford.edu/main_notes.pdf

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🔗 Machine Learning from Scratch by Danny Friedman

This book is for readers looking to learn new #machinelearning 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.

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Introduction to Machine Learning Class Notes by Huy Nguyen https://www.cs.cmu.edu/~hn1/documents/machine-learning/notes.pdf

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#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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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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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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👨🏻‍💻 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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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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