├── README.md ├── Mathematics.md ├── Machine Learning.md └── LICENSE /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | 10 | 11 | 12 | # Awesome-Data-Science-ML-Resources 13 | [![Contributors][contributors-shield]][contributors-url] 14 | [![LinkedIn][linkedin-shield]][linkedin-url] 15 | 16 | Exploring mathematics, statistics, data science, and ML through curated resources. 17 | 18 | Welcome to the "Awesome Data Science and ML Resources" repository! 🚀 19 | 20 | Listed here are a variety of resources I've encountered through my journey of learning in the fields mentioned. Whether you're just starting your journey or you're a seasoned practitioner, you'll surely find something here that helps deepen your understanding and advance your skills in the world of mathematics, statistics, data science, and machine learning. 21 | 22 | 📚 Topics Covered: 23 | 24 | - **[Mathematics](Mathematics.md):** Explore foundational mathematical concepts that underpin data science and AI. 25 | - **Statistics:** Delve into statistical principles crucial for data analysis and modeling. 26 | - **Python Programming:** Master Python, the go-to language for data scientists and AI engineers. 27 | - **[Machine Learning](Machine%20Learning.md) (ML):** Discover algorithms and techniques for predictive analytics. 28 | 29 | 🔗 Resource Categories: 30 | 31 | - **Books 📖** 32 | - **Online Courses 🎓** 33 | - **Youtube Pages and Playlists 📺** 34 | - **Blogs & Articles 📰** 35 | 36 | Feel free to explore, contribute, and share your own favorite resources. 37 | 38 | 🌟 Star this repository to bookmark it for future reference. 39 | 40 | Happy learning and coding! 41 | 42 | 43 | 44 | 45 | 46 | [contributors-shield]: https://img.shields.io/github/contributors/AmirV97/Awesome-ML-Resources.svg?style=for-the-badge 47 | [contributors-url]: https://github.com/AmirV97/Awesome-ML-Resources/graphs/contributors 48 | [linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=for-the-badge&logo=linkedin&colorB=555 49 | [linkedin-url]: https://www.linkedin.com/in/amir-m-vahdani-0847991a7/ 50 | -------------------------------------------------------------------------------- /Mathematics.md: -------------------------------------------------------------------------------- 1 | # Mathematics 2 | 3 | ## ML/Programming Perspective 4 | - *A Programmer's Introduction to Mathematics* by Jeremy Kun [2nd Edition](https://pimbook.org/) 5 | - A comprehensive guide to mathematics tailored for programmers, with a focus on its applications in machine learning and programming. 6 | 7 | - *Mathematics for Machine Learning* by Marc Peter Deisenroth, A. Aldo Faisat & Cheng Soon Ong [Online Edition](https://mml-book.github.io/) 8 | - An online resource that covers essential mathematical concepts essential for machine learning practitioners. 9 | 10 | - *Calculus for Machine Learning* by Stefania Christina & Mehreen Saeed [1st Edition](https://machinelearningmastery.com/calculus-for-machine-learning/) 11 | - Explore calculus from a machine learning perspective, with practical examples and applications. 12 | 13 | ## Linear Algebra 14 | ### Textbooks 15 | - *Elementary Linear Algebra* by Ron Larson [8th Edition](https://www.amazon.com/Elementary-Linear-Algebra-Ron-Larson/dp/1305658000) 16 | - A comprehensive textbook covering linear algebra concepts, suitable for beginners and advanced learners. 17 | 18 | - *Introduction to Linear Algebra* by Gilbert Strang [6th Edition](https://math.mit.edu/~gs/linearalgebra/ila6/indexila6.html) 19 | - An accessible introduction to linear algebra, widely used in machine learning and data science courses. 20 | 21 | - *Linear Algebra and Its Applications* by David C. Lay, Steven R. Lay & Judi J. McDonald [6th Edition](https://www.amazon.com/Linear-Algebra-Its-Applications-Global/dp/1292351217) 22 | - A textbook that combines theory with practical applications of linear algebra. 23 | 24 | ## Calculus 25 | ### Textbooks 26 | - *The Hitchhiker's Guide to Calculus* by Michael Spivak [Reprint Edition](https://www.amazon.com/Hitchhikers-Calculus-Classroom-Resource-Materials/dp/1470449625) 27 | - An engaging guide to calculus concepts, suitable for both beginners and those looking for a fresh perspective. 28 | 29 | - *Calculus* by Michael Spivak [4th Edition](https://theswissbay.ch/pdf/Gentoomen%20Library/Maths/Calculus/Michael%20Spivak%20-%20Calculus.pdf) 30 | - A classic textbook that delves deep into calculus theory and applications. 31 | 32 | - *Calculus, A Complete Course* by Robert A. Adams & Christopher Esser [9th Edition](https://www.amazon.com/Calculus-Christopher-Essex-author-Robert/dp/0134154363) 33 | - A comprehensive calculus course with a focus on problem-solving and real-world applications. 34 | 35 | ## Combinatorics & Graph Theory 36 | 37 | ### Papers and Blog Posts 38 | - *Why Graph Theory Is Cooler than You Thought* by Sid Arciadacono [Towards Data Science](https://towardsdatascience.com/why-graph-theory-is-cooler-than-you-thought-4df73e2a4950) 39 | - Explore the fascinating world of graph theory and its relevance in data science and AI through this engaging blog post. 40 | 41 | - *Introduction to Graph Machine Learning* by Clémentine Fourrier [Hugging Face blog](https://huggingface.co/blog/intro-graphml) 42 | - Delve into the foundations of graph machine learning in this informative blog post. 43 | 44 | - *What is Graph Theory, and Why Should You Care?* by Vegard Flovik [KDnuggets](https://www.kdnuggets.com/2021/01/graph-theory-why-care.html) 45 | - Discover the practical applications and significance of graph theory in the world of data science and machine learning. 46 | 47 | ### Textbooks 48 | - *A Textbook of Graph Theory* by R. Balakrishnan & K. Balakrishnan [2nd Edition](http://meskc.ac.in/wp-content/uploads/2018/12/A-Textbook-of-Graph-Theory-R.-Balakrishnan-K.-Ranganathan.pdf) 49 | - A comprehensive textbook covering the fundamental concepts of graph theory, suitable for both beginners and advanced learners. 50 | 51 | - *Combinatorics and Graph Theory* by John M. Harris, Jeffrey L. Hirst & Michael J. Mossinghoff [2nd Edition](https://doc.lagout.org/science/0_Computer%20Science/3_Theory/Graph%20Theory/Combinatorics%20and%20Graph%20Theory.pdf) 52 | - This textbook offers a detailed exploration of combinatorics and graph theory, essential for those interested in data analysis and network science. 53 | 54 | - *Introductory Combinatorics* by Richard A. Brualdi [5th Edition](https://www.amazon.com/Introductory-Combinatorics-5th-Richard-Brualdi/dp/0136020402) 55 | - An introductory text that provides a solid foundation in combinatorial mathematics, applicable in various data science and optimization problems. 56 | 57 | ## Miscellaneous 58 | - *An Introduction to Kolmogorov Complexity and Its Applications* by Ming Li & Paul Vitányi [4th Edition](https://link.springer.com/book/10.1007/978-3-030-11298-1) 59 | - Explore the concept of Kolmogorov complexity and its applications in data compression, information theory, and algorithmic complexity. 60 | 61 | - *A Survey of Topological Machine Learning Methods* by Felix Hensel, Michael Moor & Bastien Rieck [2021 Paper](https://www.frontiersin.org/articles/10.3389/frai.2021.681108/full) 62 | - This paper surveys the growing field of topological machine learning, offering insights into its principles and applications in data analysis. 63 | -------------------------------------------------------------------------------- /Machine Learning.md: -------------------------------------------------------------------------------- 1 | # Machine Learning 2 | ## Courses & Specializations 3 | - *Machine Learning* by deeplearning.ai [Coursera](https://www.coursera.org/specializations/machine-learning-introduction) 4 | 5 | ## Textbooks 6 | - *An Introduction to Statistical Learning (ISLP)* by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani & Johnathan Taylor [Online Edition](https://www.statlearning.com), [Youtube Playlist](https://www.youtube.com/playlist?list=PLoROMvodv4rOzrYsAxzQyHb8n_RWNuS1e) 7 | - *Approaching Almost Any ML Problem* by Abhishek Thakur [Online Edition](https://docdrop.org/download_annotation_doc/AAAMLP-569to.pdf) 8 | - *Bayesian Analysis with Python* by Osvaldo Martin [2nd Edition](https://www.amazon.co.uk/Bayesian-Analysis-Python-Osvaldo-Martin/dp/1785883801) 9 | - *Causal Inference in Python* by Matheus Facure [1st Edition](https://www.oreilly.com/library/view/causal-inference-in/9781098140243/) 10 | - *Dive into Deep Learning (D2DL)* by A. Zhang, Z. C. Lipton, M. Li, & A. J. Smola [Online Editon](http://d2l.ai/index.html) 11 | - *Python for Data Analysis* by Wes McKinney [Online 3rd Edition](https://wesmckinney.com/book/) 12 | - *Deep Learning* by I. Goodfellow, Y. Bengio & A. Courville [Online Edition](https://www.deeplearningbook.org) 13 | - *Deep Learning for Computer Vision* by Adrian Rosenbrock [1st Edition](https://pyimagesearch.com/deep-learning-computer-vision-python-book/) 14 | - *Deep Learning with Python* by François Chollet [2nd Edition](https://www.manning.com/books/deep-learning-with-python) 15 | - *Deep Learning for Coders with FastAI and PyTorch* by Jeremy Howard & Sylvian Gugger [1st Edition](https://www.amazon.co.uk/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527) 16 | - *Deep Learning with PyTorch* by Eli Stevens, Luca Antiga & Thomas Viehmann [1st Edition](https://www.manning.com/books/deep-learning-with-pytorch) 17 | - *The Elements of Statistical Learning (ESL)* by Tresvor Hastie, Robert Tibshirani & Jerome Friedman [Online 2nd Edition](https://hastie.su.domains/Papers/ESLII.pdf) 18 | - *Generative Deep Learning* by David Foster [1st Edition](https://www.oreilly.com/library/view/generative-deep-learning/9781492041931/) 19 | - *Geometric Deep Learning* by M. Bronstein, J. Bruna, T. Cohen & P. Veličković [Online Editon](https://geometricdeeplearning.com) 20 | - *Grokking Machine Learning* by Luis Serrano [1st Edition](https://www.manning.com/books/grokking-machine-learning) 21 | - *Hands-On Graph Neural Networks Using Python* by Maxime Labonne [1st Edition](https://www.amazon.co.uk/Hands-Graph-Neural-Networks-Python-ebook/dp/B0BNNVZ3M2) 22 | - *Hands-On Generative Adversarial Networks with PyTorch 1.x* by John Hany & Greg Walters [1st Edition](https://www.packtpub.com/product/hands-on-generative-adversarial-networks-with-pytorch-1x/9781789530513) 23 | - *Hands-On Machine Learning with Scikit-Learn, Keras and Tensorflow* by Aurélien Géron [2nd Edition](https://www.oreilly.com/library/view/hands-on-machine-learning/9781491962282/) 24 | - *Interpretable Machine Learning* by Christoph Molnar [2nd Edition](https://christophm.github.io/interpretable-ml-book/) 25 | - *Interpreting Machine Learning Models with SHAP* by Christoph Molnar [1st Edition](https://christophmolnar.com/books/shap/) 26 | - *Introduction to Conformal Prediction with Python* by Christoph Molnar [1st Edition](https://christophmolnar.com/books/conformal-prediction/) 27 | - *Introduction to Machine Learning with Python* by Andreas C. Müller, Sarah Guido [1st Edition](https://www.oreilly.com/library/view/introduction-to-machine/9781449369880/) 28 | - *Machine Learning Projects* by Brain Boucheron & Lisa Tagliaferri [Online Edition](https://assets.digitalocean.com/books/python/machine-learning-projects-python.pdf) 29 | - *Machine Learning Design Patterns* by Valliapa Lakshmanan, Sara Robinson & Michael Munn [1st Edition](https://www.oreilly.com/library/view/machine-learning-design/9781098115777/) 30 | - *Machine Learning for Healthcare Applications* by S. H. Mohanty, G. Nalinipriya, O. P. Jenna & A. Sarkar [1st Edition](https://www.amazon.com/Machine-Learning-Healthcare-Applications-Mohanty-ebook/dp/B0947KPNYY) 31 | - *Machine Learning with PyTorch and Scikit-Learn* by Sebastian Raschka, Yuxi Liu & Vahid Mirjalili [1st Edition](https://www.amazon.com/Machine-Learning-PyTorch-Scikit-Learn-learning/dp/1801819319) 32 | - *Machine Learning: A Probabilistic Perspective (MLAPP)* by Kevin P. Murphy [Online Edition](http://noiselab.ucsd.edu/ECE228/Murphy_Machine_Learning.pdf) 33 | - *Mathematic for Machine Learning (MML)* by Marc Peter Deisenroth, A. Aldo Faisal & Cheng Soon Ong [Online Edition](https://mml-book.github.io/book/mml-book.pdf), [Solutions](https://dokumen.pub/mathematics-for-machine-learning-mml-official-solutions-instructors-solution-manual-9781108455145-9781108470049-9781108569323-1108470041-1108569323-110845514x.html) 34 | - *Modeling Mindsets: The Many Cultures of Learning From Data* by Christoph Molnar [1st Edition](https://christophmolnar.com/books/modeling-mindsets/) 35 | - *Modern Time Series Forecasting with Python* by Manu Joseph [1st Edition](https://www.packtpub.com/product/modern-time-series-forecasting-with-python/9781803246802) 36 | - *Natural Language Processing with Transformers* by Lewis Tunstall, Leandro von Werra & Thomas Wolf [1st Edition](https://www.oreilly.com/library/view/natural-language-processing/9781098136789/) 37 | - *Pattern Recognition and Machine Learning (PRML)* by Christopher M. Bishop [Online Edition](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf) 38 | - *Probabilistic Machine Learning: An Introduction (ProbML)* by Kevin P. Murphy [Online Edition](https://probml.github.io/pml-book/book1.html) 39 | - *Probabilistic Machine Learning: Advanced Topics (ProbML)* by Kevin P. Murphy [Online Edition](https://probml.github.io/pml-book/book2.html) 40 | - *Programming PyTorch for Deep Learning* by Ian Pointer [1st Edition](https://www.oreilly.com/library/view/programming-pytorch-for/9781492045342/) 41 | - *PyTorch Computer Vision Cookbook* by Michael Avendi [1st Edition](https://www.amazon.com/PyTorch-Computer-Vision-Cookbook-computer/dp/1838644830) 42 | - *The Pandas Workshop* by B. Bateman, S. Basak, T. V. Joseph & W. So [1st Edition](https://www.packtpub.com/product/the-pandas-workshop/9781800208933) 43 | - *Transformers for Natural Language Processing* by Denis Rothman [1st Edition](https://www.packtpub.com/product/transformers-for-natural-language-processing/9781800565791) 44 | - *Understanding Machine Learning: from Theory to Algorithms (UDL)* by Shai Ben-David and Shai Shalev-Shwartz [Online Edition](https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf) 45 | 46 | ## Miscellaneous 47 | - *A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification* by Anastasios N. Angelopoulos & Stephen Bates [Paper](https://arxiv.org/abs/2107.07511) 48 | - *Hands-On Bayesian Neural Networks - A Tutorial for Deep Learning Users* by L. V. Jospin et. al. [Paper](https://arxiv.org/abs/2007.06823) 49 | - *Machine Learning for Probabilistic Prediction* by Valery Manokhin [Thesis](https://www.researchgate.net/publication/361515440_Machine_Learning_for_Probabilistic_Prediction_PhD_thesis_VALERY_MANOKHIN) 50 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Creative Commons Legal Code 2 | 3 | CC0 1.0 Universal 4 | 5 | CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE 6 | LEGAL SERVICES. DISTRIBUTION OF THIS DOCUMENT DOES NOT CREATE AN 7 | ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES THIS 8 | INFORMATION ON AN "AS-IS" BASIS. CREATIVE COMMONS MAKES NO WARRANTIES 9 | REGARDING THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS 10 | PROVIDED HEREUNDER, AND DISCLAIMS LIABILITY FOR DAMAGES RESULTING FROM 11 | THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS PROVIDED 12 | HEREUNDER. 13 | 14 | Statement of Purpose 15 | 16 | The laws of most jurisdictions throughout the world automatically confer 17 | exclusive Copyright and Related Rights (defined below) upon the creator 18 | and subsequent owner(s) (each and all, an "owner") of an original work of 19 | authorship and/or a database (each, a "Work"). 20 | 21 | Certain owners wish to permanently relinquish those rights to a Work for 22 | the purpose of contributing to a commons of creative, cultural and 23 | scientific works ("Commons") that the public can reliably and without fear 24 | of later claims of infringement build upon, modify, incorporate in other 25 | works, reuse and redistribute as freely as possible in any form whatsoever 26 | and for any purposes, including without limitation commercial purposes. 27 | These owners may contribute to the Commons to promote the ideal of a free 28 | culture and the further production of creative, cultural and scientific 29 | works, or to gain reputation or greater distribution for their Work in 30 | part through the use and efforts of others. 31 | 32 | For these and/or other purposes and motivations, and without any 33 | expectation of additional consideration or compensation, the person 34 | associating CC0 with a Work (the "Affirmer"), to the extent that he or she 35 | is an owner of Copyright and Related Rights in the Work, voluntarily 36 | elects to apply CC0 to the Work and publicly distribute the Work under its 37 | terms, with knowledge of his or her Copyright and Related Rights in the 38 | Work and the meaning and intended legal effect of CC0 on those rights. 39 | 40 | 1. Copyright and Related Rights. A Work made available under CC0 may be 41 | protected by copyright and related or neighboring rights ("Copyright and 42 | Related Rights"). Copyright and Related Rights include, but are not 43 | limited to, the following: 44 | 45 | i. the right to reproduce, adapt, distribute, perform, display, 46 | communicate, and translate a Work; 47 | ii. moral rights retained by the original author(s) and/or performer(s); 48 | iii. publicity and privacy rights pertaining to a person's image or 49 | likeness depicted in a Work; 50 | iv. rights protecting against unfair competition in regards to a Work, 51 | subject to the limitations in paragraph 4(a), below; 52 | v. rights protecting the extraction, dissemination, use and reuse of data 53 | in a Work; 54 | vi. database rights (such as those arising under Directive 96/9/EC of the 55 | European Parliament and of the Council of 11 March 1996 on the legal 56 | protection of databases, and under any national implementation 57 | thereof, including any amended or successor version of such 58 | directive); and 59 | vii. other similar, equivalent or corresponding rights throughout the 60 | world based on applicable law or treaty, and any national 61 | implementations thereof. 62 | 63 | 2. Waiver. To the greatest extent permitted by, but not in contravention 64 | of, applicable law, Affirmer hereby overtly, fully, permanently, 65 | irrevocably and unconditionally waives, abandons, and surrenders all of 66 | Affirmer's Copyright and Related Rights and associated claims and causes 67 | of action, whether now known or unknown (including existing as well as 68 | future claims and causes of action), in the Work (i) in all territories 69 | worldwide, (ii) for the maximum duration provided by applicable law or 70 | treaty (including future time extensions), (iii) in any current or future 71 | medium and for any number of copies, and (iv) for any purpose whatsoever, 72 | including without limitation commercial, advertising or promotional 73 | purposes (the "Waiver"). Affirmer makes the Waiver for the benefit of each 74 | member of the public at large and to the detriment of Affirmer's heirs and 75 | successors, fully intending that such Waiver shall not be subject to 76 | revocation, rescission, cancellation, termination, or any other legal or 77 | equitable action to disrupt the quiet enjoyment of the Work by the public 78 | as contemplated by Affirmer's express Statement of Purpose. 79 | 80 | 3. Public License Fallback. Should any part of the Waiver for any reason 81 | be judged legally invalid or ineffective under applicable law, then the 82 | Waiver shall be preserved to the maximum extent permitted taking into 83 | account Affirmer's express Statement of Purpose. In addition, to the 84 | extent the Waiver is so judged Affirmer hereby grants to each affected 85 | person a royalty-free, non transferable, non sublicensable, non exclusive, 86 | irrevocable and unconditional license to exercise Affirmer's Copyright and 87 | Related Rights in the Work (i) in all territories worldwide, (ii) for the 88 | maximum duration provided by applicable law or treaty (including future 89 | time extensions), (iii) in any current or future medium and for any number 90 | of copies, and (iv) for any purpose whatsoever, including without 91 | limitation commercial, advertising or promotional purposes (the 92 | "License"). The License shall be deemed effective as of the date CC0 was 93 | applied by Affirmer to the Work. Should any part of the License for any 94 | reason be judged legally invalid or ineffective under applicable law, such 95 | partial invalidity or ineffectiveness shall not invalidate the remainder 96 | of the License, and in such case Affirmer hereby affirms that he or she 97 | will not (i) exercise any of his or her remaining Copyright and Related 98 | Rights in the Work or (ii) assert any associated claims and causes of 99 | action with respect to the Work, in either case contrary to Affirmer's 100 | express Statement of Purpose. 101 | 102 | 4. Limitations and Disclaimers. 103 | 104 | a. No trademark or patent rights held by Affirmer are waived, abandoned, 105 | surrendered, licensed or otherwise affected by this document. 106 | b. Affirmer offers the Work as-is and makes no representations or 107 | warranties of any kind concerning the Work, express, implied, 108 | statutory or otherwise, including without limitation warranties of 109 | title, merchantability, fitness for a particular purpose, non 110 | infringement, or the absence of latent or other defects, accuracy, or 111 | the present or absence of errors, whether or not discoverable, all to 112 | the greatest extent permissible under applicable law. 113 | c. Affirmer disclaims responsibility for clearing rights of other persons 114 | that may apply to the Work or any use thereof, including without 115 | limitation any person's Copyright and Related Rights in the Work. 116 | Further, Affirmer disclaims responsibility for obtaining any necessary 117 | consents, permissions or other rights required for any use of the 118 | Work. 119 | d. Affirmer understands and acknowledges that Creative Commons is not a 120 | party to this document and has no duty or obligation with respect to 121 | this CC0 or use of the Work. 122 | --------------------------------------------------------------------------------