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https://github.com/azizp128/data-science-awesome-reference

Daftar referensi tautan-tautan berguna untuk mempelajari tentang Data Science, Machine Learning, dan lainnya. Reference list of useful links to learn about Data Science, Machine Learning and more.
https://github.com/azizp128/data-science-awesome-reference

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Daftar referensi tautan-tautan berguna untuk mempelajari tentang Data Science, Machine Learning, dan lainnya. Reference list of useful links to learn about Data Science, Machine Learning and more.

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README 

        
# Data-Science-Awesome-References

Daftar referensi tautan-tautan berguna untuk mempelajari tentang Data Science, Machine Learning, dan lainnya.  



Reference list of useful links to learn about Data Science, Machine Learning and more.




## MACHINE LEARNING

### Introduction

#### Articles

[Intro Data Science with Siuba](https://learn.siuba.org/intro-data-science/)



[AI Expert Roadmap](https://i.am.ai/roadmap/#note)



[Introduction to AI](https://www.elementsofai.com/)



[Welcome to Introduction to Machine Learning for Coders! taught by Jeremy Howard](https://course18.fast.ai/ml.html)



[Machine Learning From Scratch](https://dafriedman97.github.io/mlbook/content/introduction.html)



[FOUNDATIONS OF MACHINE LEARNING by Bloomberg](https://bloomberg.github.io/foml/#homeworkslave)



[Introduction to Machine Learning](https://sebastianraschka.com/resources/ml-lectures-1/)



[Machine learning cheat sheet](https://github.com/soulmachine/machine-learning-cheat-sheet)



#### MOOC

[Create machine learning models by Microsoft](https://docs.microsoft.com/en-gb/learn/paths/create-machine-learn-models/)



[Coursera : Machine Learning by AndrewNg](https://www.coursera.org/learn/machine-learning)



[Stanford CS 229 ― Machine Learning](https://stanford.edu/~shervine/teaching/cs-229/)



[Coursera Machine Learning Foundations: A Case Study Approach](http://bit.ly/2Y11VsB)



[Introduction to Data Science and Machine Learning](https://www.confetti.ai/)



[The Missing Semester of Your CS Education by MIT](https://missing.csail.mit.edu/)



[Stanford CS229: Machine Learning | Autumn 2018](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU)



[Introduction to Machine Learning by Google](https://developers.google.com/machine-learning/crash-course/ml-intro)



[Tabular Data by Machine Learning University](https://www.youtube.com/playlist?list=PL8P_Z6C4GcuVQZCYf_ZnMoIWLLKGx9Mi2)



[Stat 451: Intro to Machine Learning (Fall 2020)](https://www.youtube.com/playlist?list=PLTKMiZHVd_2KyGirGEvKlniaWeLOHhUF3)



### ML Projects 

[500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code](https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code)



[Best open-access datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others.](https://medium.com/towards-artificial-intelligence/best-datasets-for-machine-learning-data-science-computer-vision-nlp-ai-c9541058cf4f)



[Guide to Awesome Machine Learning Projects](https://github.com/dair-ai/awesome-ML-projects-guide)



[The Movies Dataset](https://www.kaggle.com/rounakbanik/the-movies-dataset)



[ML X ART creative machine learning experiments](https://mlart.co/)



[ML ArtLine](https://github.com/vijishmadhavan/ArtLine)



[ML Example Projects by Paperwithcode](https://paperswithcode.com/)



[10 ML Beginner Projects](https://twitter.com/svpino/status/1331563952967475200)



### Supervised Learning

#### Regression

[Ten minutes to learn Linear regression for dummies!!!](https://medium.com/@venkateshpnk22/ten-minutes-to-learn-linear-regression-for-dummies-5469038f4781)



[Statistics 101: Linear Regression, The Very Basics 📈](https://www.youtube.com/watch?v=ZkjP5RJLQF4)



[Linear Regression Concept](https://www.instagram.com/p/CH5bz1RDT7M/?igshid=1c0ubhbflm4mr)



[The Simple Linear Regression Explanation](https://twitter.com/oliverjumpertz/status/1346544095670525952)



#### Classification

[Machine Learning Decision Tree](https://www.hackerearth.com/practice/machine-learning/machine-learning-algorithms/ml-decision-tree/tutorial/)



[Classification with TensorFlow](https://www.tensorflow.org/tutorials/keras/classification)



[How to Get Started With Recommender Systems](https://machinelearningmastery.com/recommender-systems-resources/)



### Unsupervised Learning

#### NLP

[Machine Learning and NLP for Students and Practitioners](https://twitter.com/omarsar0/status/1344633106867884033)



[Recommendations for Getting Started with NLP](https://elvissaravia.substack.com/p/my-recommendations-for-getting-started)



[Standford CS224n: Natural Language Processing with Deep Learning](http://web.stanford.edu/class/cs224n/)



[CMU Multilingual NLP 2020](https://www.youtube.com/playlist?list=PL8PYTP1V4I8CHhppU6n1Q9-04m96D9gt5)



[How to use ML to search photos by natural language?](https://twitter.com/haltakov/status/1351271372463497217)



[Tracking Progress in Natural Language Processing](http://nlpprogress.com/)



[NLP Best Practices](https://github.com/microsoft/nlp-recipes)



[Modern Deep Learning Techniques Applied to Natural Language Processing](https://nlpoverview.com/)



[Build, train and deploy state of the art models powered by the reference open source in natural language processing.](https://huggingface.co/)



[The Big Bad NLP Database](https://datasets.quantumstat.com/)



[A Survey of Surveys (NLP & ML)](https://github.com/NiuTrans/ABigSurvey)



### Neural Networks

[A Recipe for Training Neural Networks](http://karpathy.github.io/2019/04/25/recipe/)



[Convolutional Neural Nets: Foundations, Computations, and New Applications by Cornell University](https://arxiv.org/abs/2101.04869)



[Non-linearities in Neural Networks](https://twitter.com/svpino/status/1351156045620588547)



### Deep Learning

[MIT 6.S191 Introduction to Deep Learning](http://introtodeeplearning.com/)



[Literature of Deep Learning for Graphs](https://github.com/DeepGraphLearning/LiteratureDL4Graph)



[Awesome Pytorch List](https://github.com/bharathgs/Awesome-pytorch-list#tutorials-books--examples)



[ML Visuals](https://github.com/dair-ai/ml-visuals)



[Deep Learning (with PyTorch)](https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq)



[Deep Learning for Computer Vision](https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r)



[Lectures for UC Berkeley CS 285: Deep Reinforcement Learning.](https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc)



[Full Stack Deep Learning Youtube Channel](https://www.youtube.com/channel/UCVchfoB65aVtQiDITbGq2LQ)



## PROGRAMMING LANGUAGES

### PYTHON

#### Beginner

[Python Basics : Learn Python Programming](https://pythonbasics.org/)



[Coursera Algorithms Specialization](https://www.coursera.org/specializations/algorithms)



[Google's Python Class](https://developers.google.com/edu/python)



[PYTHONINDO](https://www.pythonindo.com/)



[BELAJARPYTHON](https://belajarpython.com/tutorial/apa-itu-python)



[FreeCodeCamp Introduction to Python for Everybody](https://www.freecodecamp.org/learn/scientific-computing-with-python/python-for-everybody/)



[Python Getting Started](https://www.python.org/about/gettingstarted/)



[Python Basic Exercise for Beginners](https://pynative.com/python-basic-exercise-for-beginners/)



#### Intermediate

[Beginner Python exercises](http://www.practicepython.org/)



[Edabit Python Challenges](https://edabit.com/challenges/python3)



[NumPy Illustrated: The Visual Guide to NumPy](https://medium.com/better-programming/numpy-illustrated-the-visual-guide-to-numpy-3b1d4976de1d)



[Learning Python for Data Analysis and Visualization](https://www.udemy.com/course/learning-python-for-data-analysis-and-visualization/)



[Code Combat](https://codecombat.com)



### ML With Python

[Introduction to TensorFlow](https://www.tensorflow.org/learn)



[Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/)



[Lectures of Linear Algebra with Python](https://github.com/MacroAnalyst/Linear_Algebra_With_Python)



[Best-of Machine Learning with Python](https://github.com/ml-tooling/best-of-ml-python)



[Harvard CS109 Data Science](http://cs109.github.io/2015/pages/videos.html)



[Linear Regression in Python](https://realpython.com/linear-regression-in-python/)



[Coursera Statistics with Python Specialization](https://www.coursera.org/specializations/statistics-with-python)



[Introduction to Linear Algebra for Applied Machine Learning with Python](https://pabloinsente.github.io/intro-linear-algebra)



[Linear Regression for Absolute Beginners with Implementation in Python!](https://www.analyticsvidhya.com/blog/2020/10/linear-regression-for-absolute-beginners-with-implementation-in-python/)



## MATHEMATICS

### Linear Algebra

[Khan Academy : Linear Algebra](https://www.khanacademy.org/math/linear-algebra)



[Ritchieng : Linear Algebra for Machine Learning](https://www.ritchieng.com/linear-algebra-machine-learning/)



[DIDL : Linear Algebra](https://d2l.ai/chapter_preliminaries/linear-algebra.html)



[Pabloinsente : Linear Algebra](https://pabloinsente.github.io/intro-linear-algebra)



[MIT Open Courseware : Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/)



[Mathematics for Machine Learning - Linear Algebra](https://www.youtube.com/playlist?list=PLiiljHvN6z1_o1ztXTKWPrShrMrBLo5P3)



### Statistics & Probability

[StatQuest : Statistics Fundamentals](https://www.youtube.com/playlist?list=PLblh5JKOoLUK0FLuzwntyYI10UQFUhsY9)



[Udacity : Intro to Statistics](https://www.udacity.com/course/intro-to-statistics--st101)



[StatQuest : Linear Regression & Linear Model](https://www.youtube.com/playlist?list=PLblh5JKOoLUIzaEkCLIUxQFjPIlapw8nU)



[EDX Data Science: Probability](https://www.edx.org/course/data-science-probability)



[An Intuitive Introduction to Probability](https://www.coursera.org/learn/introductiontoprobability)



[Probabilistic Machine Learning: An Introduction](https://probml.github.io/pml-book/book1.html)



### Calculus

[EDX MathTrackX: Differential Calculus](https://courses.edx.org/courses/course-v1:AdelaideX+DiffTraX+1T2020/course/)



[ML Glossary : Calculus](https://ml-cheatsheet.readthedocs.io/en/latest/calculus.html)



[Youtube Channel KALKULUS](https://www.youtube.com/playlist?list=PLBUHpBFmQyt4qp2bBOx67y2v0U1BmtPfS)



[Mathematics for Machine Learning - Multivariate Calculus by Imperial College London](https://www.youtube.com/playlist?list=PLiiljHvN6z193BBzS0Ln8NnqQmzimTW23)



[The Matrix Calculus You Need For Deep Learning by Cornell University](https://arxiv.org/abs/1802.01528)



### Additional

[Proof Index Mathematics Course](https://proofindex.com/)



[Computer Science courses with video lectures](https://github.com/Developer-Y/cs-video-courses#math-for-computer-scientist)



[10 Machine Learning Youtube Videos by Santiago](https://twitter.com/svpino/status/1349685410600001542)



[Coursera Introduction to Complex Analysis](https://www.coursera.org/learn/complex-analysis)



[Coursera Information Theory](https://www.coursera.org/learn/information-theory)



[Coursera Data Mining Specialization](https://www.coursera.org/specializations/data-mining)



[Coursera Mathematics for Machine Learning Specialization](https://www.coursera.org/specializations/mathematics-machine-learning)



[MIT Missing Semester IAP 2020](https://www.youtube.com/playlist?list=PLyzOVJj3bHQuloKGG59rS43e29ro7I57J)



## Books

[The Hundred Page Machine Learning Book](http://themlbook.com/wiki/doku.php)



[Ebook Programming Gratis by DeepTech](https://twitter.com/deeptech_id/status/1321669306535456768)



[Mathematics for Machine Learning Ebook Pdf](http://gwthomas.github.io/docs/math4ml.pdf)



[ML / AI Books Recommendation](https://twitter.com/galuhsahid/status/1344611529656684545)



[Bayesian Books Recommendation](https://twitter.com/ChadScherrer/status/1346565926934642690)



[Programming Recommendation Books](https://twitter.com/svpino/status/1334736188608028673)



[Rekomendasi Buku Python Bahasa Indonesia by DeepTech Twitter](https://twitter.com/pacmannai/status/1331918966722269191)



[Amazon Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD](https://www.amazon.com/gp/product/B08C2KM7NR/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=shiftedup-20&creative=9325&linkCode=as2&creativeASIN=B08C2KM7NR&linkId=238e2afdcd34172b28f0e6c18c88c574)



## Other References

[Data Science Learning Path](https://github.com/data-folks/data-science-learning-path)



[Path to a free self-taught education in Data Science! by Open Source Society University](https://github.com/ossu/data-science)



[Course Recommendations for Introductory Machine Learning](https://elvissaravia.substack.com/p/course-recommendations-for-introductory)



[Homemade Machine Learning](https://github.com/trekhleb/homemade-machine-learning)



[Kumpulan kuliah bagus di Stanford untuk menjadi data scientist yang sakti mandraguna](https://twitter.com/aliakbars/status/1345761027246485504)



[Machine Learning, Deep Learning, and Computer Vision References](https://github.com/mheriyanto/Machine-Learning-and-Computer-Vision-References)



[15 Trending Data Science GitHub Repositories you can not miss in 2017](https://www.analyticsvidhya.com/blog/2017/12/15-data-science-repositories-github-2017/)



[10 Popular Data Science Resources on Github](https://towardsdatascience.com/10-popular-data-science-resources-on-github-7ae288ff4a75)



[Github Data-Science-References](https://github.com/ekosaputro09/Data-Science-References)



[Github Data Science Collected Resources](https://github.com/tirthajyoti/Data-science-best-resources)



[Github Data-Science-Resources](https://github.com/storieswithsiva/Data-Science-Resources)



[50 FREE Artificial Intelligence, Computer Science, Engineering and Programming Courses from the Ivy League Universities](https://twitter.com/LearnersBucket/status/1316983042146185218)