{"id":25109,"url":"https://github.com/azizp128/data-science-awesome-reference","name":"data-science-awesome-reference","description":"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.","projects_count":122,"last_synced_at":"2026-09-04T11:00:23.978Z","repository":{"id":153136430,"uuid":"277056145","full_name":"azizp128/data-science-awesome-reference","owner":"azizp128","description":"Daftar referensi tautan-tautan berguna untuk mempelajari tentang Data Science, Machine Learning, dan lainnya.  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LEARNING","PROGRAMMING LANGUAGES","MATHEMATICS","Books","Other References"],"sub_categories":["Unsupervised Learning","PYTHON","Introduction","ML With Python","Deep Learning","Additional","Statistics \u0026 Probability","Linear Algebra","Neural Networks","ML Projects","Supervised Learning","Calculus"],"readme":"# Data-Science-Awesome-References\nDaftar referensi tautan-tautan berguna untuk mempelajari tentang Data Science, Machine Learning, dan lainnya.  \n\nReference list of useful links to learn about Data Science, Machine Learning and more.\n\u003cimg src=\"https://github.com/azizp128/data-science-awesome-reference/blob/master/121649307_123755559256148_7033582196602709082_n.jpg\"\u003e\n\n## MACHINE LEARNING\n### Introduction\n#### Articles\n[Intro Data Science with Siuba](https://learn.siuba.org/intro-data-science/)\n\n[AI Expert Roadmap](https://i.am.ai/roadmap/#note)\n\n[Introduction to AI](https://www.elementsofai.com/)\n\n[Welcome to Introduction to Machine Learning for Coders! taught by Jeremy Howard](https://course18.fast.ai/ml.html)\n\n[Machine Learning From Scratch](https://dafriedman97.github.io/mlbook/content/introduction.html)\n\n[FOUNDATIONS OF MACHINE LEARNING by Bloomberg](https://bloomberg.github.io/foml/#homeworkslave)\n\n[Introduction to Machine Learning](https://sebastianraschka.com/resources/ml-lectures-1/)\n\n[Machine learning cheat sheet](https://github.com/soulmachine/machine-learning-cheat-sheet)\n\n\n\n#### MOOC\n[Create machine learning models by Microsoft](https://docs.microsoft.com/en-gb/learn/paths/create-machine-learn-models/)\n\n[Coursera : Machine Learning by AndrewNg](https://www.coursera.org/learn/machine-learning)\n\n[Stanford CS 229 ― Machine Learning](https://stanford.edu/~shervine/teaching/cs-229/)\n\n[Coursera Machine Learning Foundations: A Case Study Approach](http://bit.ly/2Y11VsB)\n\n[Introduction to Data Science and Machine Learning](https://www.confetti.ai/)\n\n[The Missing Semester of Your CS Education by MIT](https://missing.csail.mit.edu/)\n\n[Stanford CS229: Machine Learning | Autumn 2018](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU)\n\n[Introduction to Machine Learning by Google](https://developers.google.com/machine-learning/crash-course/ml-intro)\n\n[Tabular Data by Machine Learning University](https://www.youtube.com/playlist?list=PL8P_Z6C4GcuVQZCYf_ZnMoIWLLKGx9Mi2)\n\n[Stat 451: Intro to Machine Learning (Fall 2020)](https://www.youtube.com/playlist?list=PLTKMiZHVd_2KyGirGEvKlniaWeLOHhUF3)\n\n\n### ML Projects \n[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)\n\n[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)\n\n[Guide to Awesome Machine Learning Projects](https://github.com/dair-ai/awesome-ML-projects-guide)\n\n[The Movies Dataset](https://www.kaggle.com/rounakbanik/the-movies-dataset)\n\n[ML X ART creative machine learning experiments](https://mlart.co/)\n\n[ML ArtLine](https://github.com/vijishmadhavan/ArtLine)\n\n[ML Example Projects by Paperwithcode](https://paperswithcode.com/)\n\n[10 ML Beginner Projects](https://twitter.com/svpino/status/1331563952967475200)\n\n### Supervised Learning\n#### Regression\n[Ten minutes to learn Linear regression for dummies!!!](https://medium.com/@venkateshpnk22/ten-minutes-to-learn-linear-regression-for-dummies-5469038f4781)\n\n[Statistics 101: Linear Regression, The Very Basics 📈](https://www.youtube.com/watch?v=ZkjP5RJLQF4)\n\n[Linear Regression Concept](https://www.instagram.com/p/CH5bz1RDT7M/?igshid=1c0ubhbflm4mr)\n\n[The Simple Linear Regression Explanation](https://twitter.com/oliverjumpertz/status/1346544095670525952)\n\n#### Classification\n[Machine Learning Decision Tree](https://www.hackerearth.com/practice/machine-learning/machine-learning-algorithms/ml-decision-tree/tutorial/)\n\n[Classification with TensorFlow](https://www.tensorflow.org/tutorials/keras/classification)\n\n[How to Get Started With Recommender Systems](https://machinelearningmastery.com/recommender-systems-resources/)\n\n\n### Unsupervised Learning\n#### NLP\n[Machine Learning and NLP for Students and Practitioners](https://twitter.com/omarsar0/status/1344633106867884033)\n\n[Recommendations for Getting Started with NLP](https://elvissaravia.substack.com/p/my-recommendations-for-getting-started)\n\n[Standford CS224n: Natural Language Processing with Deep Learning](http://web.stanford.edu/class/cs224n/)\n\n[CMU Multilingual NLP 2020](https://www.youtube.com/playlist?list=PL8PYTP1V4I8CHhppU6n1Q9-04m96D9gt5)\n\n[How to use ML to search photos by natural language?](https://twitter.com/haltakov/status/1351271372463497217)\n\n[Tracking Progress in Natural Language Processing](http://nlpprogress.com/)\n\n[NLP Best Practices](https://github.com/microsoft/nlp-recipes)\n\n[Modern Deep Learning Techniques Applied to Natural Language Processing](https://nlpoverview.com/)\n\n[Build, train and deploy state of the art models powered by the reference open source in natural language processing.](https://huggingface.co/)\n\n[The Big Bad NLP Database](https://datasets.quantumstat.com/)\n\n[A Survey of Surveys (NLP \u0026 ML)](https://github.com/NiuTrans/ABigSurvey)\n\n### Neural Networks\n[A Recipe for Training Neural Networks](http://karpathy.github.io/2019/04/25/recipe/)\n\n[Convolutional Neural Nets: Foundations, Computations, and New Applications by Cornell University](https://arxiv.org/abs/2101.04869)\n\n[Non-linearities in Neural Networks](https://twitter.com/svpino/status/1351156045620588547)\n\n### Deep Learning\n[MIT 6.S191 Introduction to Deep Learning](http://introtodeeplearning.com/)\n\n[Literature of Deep Learning for Graphs](https://github.com/DeepGraphLearning/LiteratureDL4Graph)\n\n[Awesome Pytorch List](https://github.com/bharathgs/Awesome-pytorch-list#tutorials-books--examples)\n\n[ML Visuals](https://github.com/dair-ai/ml-visuals)\n\n[Deep Learning (with PyTorch)](https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq)\n\n[Deep Learning for Computer Vision](https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r)\n\n[Lectures for UC Berkeley CS 285: Deep Reinforcement Learning.](https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc)\n\n[Full Stack Deep Learning Youtube Channel](https://www.youtube.com/channel/UCVchfoB65aVtQiDITbGq2LQ)\n\n\n## PROGRAMMING LANGUAGES\n### PYTHON\n#### Beginner\n[Python Basics : Learn Python Programming](https://pythonbasics.org/)\n\n[Coursera Algorithms Specialization](https://www.coursera.org/specializations/algorithms)\n\n[Google's Python Class](https://developers.google.com/edu/python)\n\n[PYTHONINDO](https://www.pythonindo.com/)\n\n[BELAJARPYTHON](https://belajarpython.com/tutorial/apa-itu-python)\n\n[FreeCodeCamp Introduction to Python for Everybody](https://www.freecodecamp.org/learn/scientific-computing-with-python/python-for-everybody/)\n\n[Python Getting Started](https://www.python.org/about/gettingstarted/)\n\n[Python Basic Exercise for Beginners](https://pynative.com/python-basic-exercise-for-beginners/)\n\n\n#### Intermediate\n[Beginner Python exercises](http://www.practicepython.org/)\n\n[Edabit Python Challenges](https://edabit.com/challenges/python3)\n\n[NumPy Illustrated: The Visual Guide to NumPy](https://medium.com/better-programming/numpy-illustrated-the-visual-guide-to-numpy-3b1d4976de1d)\n\n[Learning Python for Data Analysis and Visualization](https://www.udemy.com/course/learning-python-for-data-analysis-and-visualization/)\n\n[Code Combat](https://codecombat.com)\n\n### ML With Python\n[Introduction to TensorFlow](https://www.tensorflow.org/learn)\n\n[Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/)\n\n[Lectures of Linear Algebra with Python](https://github.com/MacroAnalyst/Linear_Algebra_With_Python)\n\n[Best-of Machine Learning with Python](https://github.com/ml-tooling/best-of-ml-python)\n\n[Harvard CS109 Data Science](http://cs109.github.io/2015/pages/videos.html)\n\n[Linear Regression in Python](https://realpython.com/linear-regression-in-python/)\n\n[Coursera Statistics with Python Specialization](https://www.coursera.org/specializations/statistics-with-python)\n\n[Introduction to Linear Algebra for Applied Machine Learning with Python](https://pabloinsente.github.io/intro-linear-algebra)\n\n[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/)\n\n\n## MATHEMATICS\n### Linear Algebra\n[Khan Academy : Linear Algebra](https://www.khanacademy.org/math/linear-algebra)\n\n[Ritchieng : Linear Algebra for Machine Learning](https://www.ritchieng.com/linear-algebra-machine-learning/)\n\n[DIDL : Linear Algebra](https://d2l.ai/chapter_preliminaries/linear-algebra.html)\n\n[Pabloinsente : Linear Algebra](https://pabloinsente.github.io/intro-linear-algebra)\n\n[MIT Open Courseware : Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/)\n\n[Mathematics for Machine Learning - Linear Algebra](https://www.youtube.com/playlist?list=PLiiljHvN6z1_o1ztXTKWPrShrMrBLo5P3)\n\n### Statistics \u0026 Probability\n[StatQuest : Statistics Fundamentals](https://www.youtube.com/playlist?list=PLblh5JKOoLUK0FLuzwntyYI10UQFUhsY9)\n\n[Udacity : Intro to Statistics](https://www.udacity.com/course/intro-to-statistics--st101)\n\n[StatQuest : Linear Regression \u0026 Linear Model](https://www.youtube.com/playlist?list=PLblh5JKOoLUIzaEkCLIUxQFjPIlapw8nU)\n\n[EDX Data Science: Probability](https://www.edx.org/course/data-science-probability)\n\n[An Intuitive Introduction to Probability](https://www.coursera.org/learn/introductiontoprobability)\n\n[Probabilistic Machine Learning: An Introduction](https://probml.github.io/pml-book/book1.html)\n\n### Calculus\n[EDX MathTrackX: Differential Calculus](https://courses.edx.org/courses/course-v1:AdelaideX+DiffTraX+1T2020/course/)\n\n[ML Glossary : Calculus](https://ml-cheatsheet.readthedocs.io/en/latest/calculus.html)\n\n[Youtube Channel KALKULUS](https://www.youtube.com/playlist?list=PLBUHpBFmQyt4qp2bBOx67y2v0U1BmtPfS)\n\n[Mathematics for Machine Learning - Multivariate Calculus by Imperial College London](https://www.youtube.com/playlist?list=PLiiljHvN6z193BBzS0Ln8NnqQmzimTW23)\n\n[The Matrix Calculus You Need For Deep Learning by Cornell University](https://arxiv.org/abs/1802.01528)\n\n\n### Additional\n[Proof Index Mathematics Course](https://proofindex.com/)\n\n[Computer Science courses with video lectures](https://github.com/Developer-Y/cs-video-courses#math-for-computer-scientist)\n\n[10 Machine Learning Youtube Videos by Santiago](https://twitter.com/svpino/status/1349685410600001542)\n\n[Coursera Introduction to Complex Analysis](https://www.coursera.org/learn/complex-analysis)\n\n[Coursera Information Theory](https://www.coursera.org/learn/information-theory)\n\n[Coursera Data Mining Specialization](https://www.coursera.org/specializations/data-mining)\n\n[Coursera Mathematics for Machine Learning Specialization](https://www.coursera.org/specializations/mathematics-machine-learning)\n\n[MIT Missing Semester IAP 2020](https://www.youtube.com/playlist?list=PLyzOVJj3bHQuloKGG59rS43e29ro7I57J)\n\n## Books\n[The Hundred Page Machine Learning Book](http://themlbook.com/wiki/doku.php)\n\n[Ebook Programming Gratis by DeepTech](https://twitter.com/deeptech_id/status/1321669306535456768)\n\n[Mathematics for Machine Learning Ebook Pdf](http://gwthomas.github.io/docs/math4ml.pdf)\n\n[ML / AI Books Recommendation](https://twitter.com/galuhsahid/status/1344611529656684545)\n\n[Bayesian Books Recommendation](https://twitter.com/ChadScherrer/status/1346565926934642690)\n\n[Programming Recommendation Books](https://twitter.com/svpino/status/1334736188608028673)\n\n[Rekomendasi Buku Python Bahasa Indonesia by DeepTech Twitter](https://twitter.com/pacmannai/status/1331918966722269191)\n\n[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\u0026tag=shiftedup-20\u0026creative=9325\u0026linkCode=as2\u0026creativeASIN=B08C2KM7NR\u0026linkId=238e2afdcd34172b28f0e6c18c88c574)\n\n## Other References\n[Data Science Learning Path](https://github.com/data-folks/data-science-learning-path)\n\n[Path to a free self-taught education in Data Science! by Open Source Society University](https://github.com/ossu/data-science)\n\n[Course Recommendations for Introductory Machine Learning](https://elvissaravia.substack.com/p/course-recommendations-for-introductory)\n\n[Homemade Machine Learning](https://github.com/trekhleb/homemade-machine-learning)\n\n[Kumpulan kuliah bagus di Stanford untuk menjadi data scientist yang sakti mandraguna](https://twitter.com/aliakbars/status/1345761027246485504)\n\n[Machine Learning, Deep Learning, and Computer Vision References](https://github.com/mheriyanto/Machine-Learning-and-Computer-Vision-References)\n\n[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/)\n\n[10 Popular Data Science Resources on Github](https://towardsdatascience.com/10-popular-data-science-resources-on-github-7ae288ff4a75)\n\n[Github Data-Science-References](https://github.com/ekosaputro09/Data-Science-References)\n\n[Github Data Science Collected Resources](https://github.com/tirthajyoti/Data-science-best-resources)\n\n[Github Data-Science-Resources](https://github.com/storieswithsiva/Data-Science-Resources)\n\n[50 FREE Artificial Intelligence, Computer Science, Engineering and Programming Courses from the Ivy League Universities](https://twitter.com/LearnersBucket/status/1316983042146185218)\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/azizp128%2Fdata-science-awesome-reference/projects"}