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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A curated list of awesome machine learning frameworks, libraries, courses, books and many more.
| Date | Stars |
|---|---|
| 2026-07-24 | 442 |
| 2026-07-25 | 442 |
| 2026-07-28 | 442 |
| 2026-07-30 | 442 |
| 2026-07-31 | 442 |
| 2026-08-06 | 443 |
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# Machine Learning Resources A curated list of awesome machine learning frameworks, libraries, courses, books and many more. Star and Fork our repository for latest update. kumpulan sumber ini untuk mempermudah untuk mempelajari machine learning, dengan bahasa indonesia yang mudah dipahami, selain itu juga terdapat dataset yang bisa dipraktekan dan ada conference yang bisa dipublish bagi yang melakukan penelitian dibidang ini. ## Table of Contents * **[Free Books](#free-books)** * **[Courses](#courses)** * **[Videos and Lectures](#videos-and-lectures)** * **[Papers](#papers)** * **[Tutorials](#tutorials)** * **[Sample Code](#sample-code)** * **[Datasets](#datasets)** * **[Conferences](#conferences-mostly-in-indonesia)** * **[Libraries](#libraries)** ### Free Books 1. [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/), by Jake VanderPlas 2. [Pengenalan Pembelajaran Mesin dan Deep Learning (Bahasa Indonesia)](https://wiragotama.github.io/ebook_machine_learning.html), by Jan Wira Gotama Putra 3. [Bayesian Reasoning and Machine Learning](http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online), by David Barber 4. [R Programming for Data Science](https://leanpub.com/rprogramming), by Roger D. Peng 5. [Think Bayes](http://greenteapress.com/wp/think-bayes/) by Allen B. Downey 6. [Mathematics for Machine Learning](https://mml-book.github.io/) by Marc Peter 7. [Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book/) by Christoph Molnar ### Courses 1. [Applied Machine Learning in Python](https://www.coursera.org/learn/python-machine-learning) by University of Michigan 2. [Machine Learning](https://www.coursera.org/learn/machine-learning) by Stanford University 3. [Machine Learning with Big Data](https://www.coursera.org/learn/big-data-machine-learning) by University of California, San Diego 4. [Principles of Machine Learning](https://www.edx.org/course/principles-of-machine-learning) by Microsoft 5. [Machine Learning for Data Science and Analytics](https://www.edx.org/course/machine-learning-data-science-analytics-columbiax-ds102x-1) by Columbia University in The City of New York 6. [Practical Deep Learning for Coders](https://course.fast.ai/) by Fast AI ### Videos and Lectures 1. [Machine Learning by Andrew Ng](https://www.youtube.com/watch?v=UzxYlbK2c7E&list=RDQMwjiIGVB03Eg) 2. [Intro to Machine Learning by Eric Grimson](https://www.youtube.com/watch?v=h0e2HAPTGF4) 3. [Machine Learning Course - CS 156](https://www.youtube.com/watch?v=mbyG85GZ0PI&list=PLD63A284B7615313A) 4. [Machine Learning from Scratch using Python](https://www.youtube.com/watch?v=tqlhXxy1-IU&list=PLkRkKTC6HZMxfLxUI36SM-3vuWJMoNpuz) 5. [Gaussian Mixture Models - The Math of Intelligence (Week 7)](https://www.youtube.com/watch?v=JNlEIEwe-Cg&t=945s) 6. [Machine Learning and Data Mining Short Series for Beginner (UC Irvine)](https://www.youtube.com/watch?v=qPhMX0vb6D8&list=PLaXDtXvwY-oDvedS3f4HW0b4KxqpJ_imw) 7. [Complete Tutorial of Apache Spark (Beginner - Intermediate)](https://www.youtube.com/watch?v=VAE0wEaYXHs&list=PLkRkKTC6HZMxAPWIqXp2bnQI_UFd0YsbC) ### Papers 1. [Local algorithms for interactive clustering](http://jmlr.org/papers/volume18/15-085/15-085.pdf) 2. [On Perturbed Proximal Gradient Algorithms](http://www.jmlr.org/papers/volume18/15-038/15-038.pdf) 3. [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](http://www.jmlr.org/papers/volume18/16-365/16-365.pdf) 4. [Nearly optimal classification for semimetrics](http://www.jmlr.org/papers/volume18/16-217/16-217.pdf) 5. [A Bayesian Framework for Learning Rule Sets for Interpretable Classification](http://www.jmlr.org/papers/volume18/16-003/16-003.pdf) ### Tutorials 1. [A Simple Approach to Predicting Customer Churn](http://blog.keyrus.co.uk/a_simple_approach_to_predicting_customer_churn.html) 2. [Complete Guide to Topic Modeling](https://nlpforhackers.io/topic-modeling/
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b7a0a1e9f36fc259, topic:awesome-list, topic:tutorial, desc:curated list
matched fp:b7a0a1e9f36fc259, topic:datasets, readme:dataset, readme:datasets