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https://github.com/datascienceid/machine-learning-resources

A curated list of awesome machine learning frameworks, libraries, courses, books and many more.
https://github.com/datascienceid/machine-learning-resources

List: machine-learning-resources

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A curated list of awesome machine learning frameworks, libraries, courses, books and many more.

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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/)

3. [K-Means Clustering in Python](https://mubaris.com/2017/10/01/kmeans-clustering-in-python/)

4. [How To Implement Naive Bayes From Scratch in Python](https://machinelearningmastery.com/naive-bayes-classifier-scratch-python/)

5. [Twitter Sentiment Analysis with NLTK](https://pythonprogramming.net/twitter-sentiment-analysis-nltk-tutorial/)



### Sample Code

1. [Practical Machine Learning with Python](https://github.com/apress/practical-ml-w-python)

2. [Data Science From Scratch](https://github.com/joelgrus/data-science-from-scratch)

3. [Introducing Data Science](https://www.manning.com/books/introducing-data-science)

4. [Machine Learning with R](https://github.com/dataspelunking/MLwR)

5. [Practical Data Science Cookbook](https://github.com/PacktPublishing/Practical-Data-Science-Cookbook-Second-Edition)

6. [Data Science with Python (Bahasa Indonesia)](https://github.com/rubiagatra/data-science-with-python)

7. [Deep Learning with PyTorch (Bahasa Indonesia)](https://github.com/rubiagatra/deep-learning-with-pytorch)

 

### Datasets

1. [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php)

2. [Kaggle Datasets](https://www.kaggle.com/datasets)

3. [IMDb Datasets](https://www.imdb.com/interfaces/)

4. [Machine Learning Datasets Repository](http://mldata.org/) 

5. [Caption Contest Data](https://github.com/nextml/caption-contest-data)

6. [Indonesia Family Life Survey](https://www.rand.org/well-being/social-and-behavioral-policy/data/FLS/IFLS.html)



### Conferences (Mostly in Indonesia)

1. [Seminar Nasional Sistem Informasi](http://sesindo.org/)

2. [International Seminar on Intelligence Technology and Its Application](http://isitia.its.ac.id/)

3. [International Conference on Advanced Computer Science and Information Systems](http://icacsis.cs.ui.ac.id/front/)

4. [International Conference on Science in Information Technology ](http://icsitech.org/)

5. [International Conference on Soft Computing, Intelligent Systems, and Information Technology](http://icsiit.petra.ac.id/)

6. [International Conference on Data and Information Science](http://icodis.org/)

7. [2019 International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)](http://iaict.org/)

8. [International Conference on Signals and Systems](http://icsigsys.org/)



### Libraries

1. [Scikit-learn](http://scikit-learn.org/)

2. [Natural Language Toolkit](http://nltk.org/)

3. [XGBoost](https://github.com/dmlc/xgboost)

4. [spaCy](https://github.com/explosion/spaCy)

5. [CNTK](https://github.com/Microsoft/CNTK)



## Contributing

Jika ingin berkontribusi dalam github ini, sangat disarankan untuk `Pull Request` namun dengan resource berbahasa indonesia.



## Frequently Ask Question (FAQ)

FAQ menjawab pertanyaan pertanyaan umum terkait repository ini mulai dari _naming convention_, pertanyaan dasar hingga pertanyaan lanjut.