From d265ff7188cef1d99ce5598bfed09e07d412c499 Mon Sep 17 00:00:00 2001 From: Yash Pawar <72196156+yashppawar@users.noreply.github.com> Date: Mon, 4 Oct 2021 02:09:19 +0530 Subject: [PATCH] =?UTF-8?q?=E2=9E=95=20add=20ML=20books=20and=20courses=20?= =?UTF-8?q?(#5726)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * add The Mechanics of Machine Learning * change url for Introduction to statistical Learning * change url for Mining of Massive Datasets * add Mathematics for Machine Learning course * add Made with ML in courses * fix position of Made with ML * remove trailing * fix trailing \ in programming-books-langs * add PDF indication to MMDS book * add author and remove author links --- books/free-programming-books-langs.md | 2 +- books/free-programming-books-subjects.md | 5 +++-- courses/free-courses-en.md | 3 +++ 3 files changed, 7 insertions(+), 3 deletions(-) diff --git a/books/free-programming-books-langs.md b/books/free-programming-books-langs.md index d230df16..06272cf2 100644 --- a/books/free-programming-books-langs.md +++ b/books/free-programming-books-langs.md @@ -1906,7 +1906,7 @@ That section got so big, we decided to split it into its own file, the [BY SUBJE ### R * [Advanced R Programming](http://adv-r.had.co.nz) - Hadley Wickham -* [An Introduction to Statistical Learning with Applications in R](http://www-bcf.usc.edu/~gareth/ISL/) - Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (PDF) +* [An Introduction to Statistical Learning with Applications in R](https://web.stanford.edu/~hastie/ISLR2/ISLRv2_website.pdf) - Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (PDF) * [Cookbook for R](http://www.cookbook-r.com) - Winston Chang * [Data Analysis and Prediction Algorithms with R](https://rafalab.github.io/dsbook/) - Rafael A. Irizarry * [Data Analysis for the Life Sciences](https://leanpub.com/dataanalysisforthelifesciences) - Rafael A Irizarry, Michael I Love *(Leanpub account or valid email requested)* diff --git a/books/free-programming-books-subjects.md b/books/free-programming-books-subjects.md index 71e8512a..4715f162 100644 --- a/books/free-programming-books-subjects.md +++ b/books/free-programming-books-subjects.md @@ -216,7 +216,7 @@ Books that cover a specific programming language can be found in the [BY PROGRA * [Internet Advertising: An Interplay among Advertisers, Online Publishers, Ad Exchanges and Web Users](http://arxiv.org/pdf/1206.1754v2.pdf) (PDF) * [Introduction to Data Science](https://docs.google.com/file/d/0B6iefdnF22XQeVZDSkxjZ0Z5VUE/edit?pli=1) - Jeffrey Stanton * [Introduction to Data Science](https://leanpub.com/datasciencebook) - Rafael A Irizarry *(Leanpub account or valid email requested)* -* [Mining of Massive Datasets](http://www.mmds.org) +* [Mining of Massive Datasets](http://infolab.stanford.edu/~ullman/mmds/book.pdf) - Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman (PDF) * [School of Data Handbook](http://schoolofdata.org/handbook/) * [Statistical inference for data science](https://leanpub.com/LittleInferenceBook/read) - Brian Caffo * [The Ultimate Guide to 12 Dimensionality Reduction Techniques (with Python codes)](https://www.analyticsvidhya.com/blog/2018/08/dimensionality-reduction-techniques-python/) - Pulkit Sharma @@ -286,7 +286,7 @@ Books that cover a specific programming language can be found in the [BY PROGRA * [A First Encounter with Machine Learning](https://www.ics.uci.edu/~welling/teaching/ICS273Afall11/IntroMLBook.pdf) (PDF) * [A Selective Overview of Deep Learning](https://arxiv.org/abs/1904.05526) - Fan, Ma, and Zhong (PDF) * [Algorithms for Reinforcement Learning](https://sites.ualberta.ca/~szepesva/papers/RLAlgsInMDPs.pdf) - Csaba Szepesvári (PDF) -* [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) - Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani +* [An Introduction to Statistical Learning](https://web.stanford.edu/~hastie/ISLR2/ISLRv2_website.pdf) - Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (PDF) * [Bayesian Reasoning and Machine Learning](http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage) * [Deep Learning](http://www.deeplearningbook.org) - Ian Goodfellow, Yoshua Bengio and Aaron Courville * [Deep Learning for Coders with Fastai and PyTorch](https://github.com/fastai/fastbook) - Jeremy Howard, Sylvain Gugger (Jupyter Notebooks) @@ -317,6 +317,7 @@ Books that cover a specific programming language can be found in the [BY PROGRA * [Speech and Language Processing (3rd Edition Draft)](https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf) - Daniel Jurafsky, James H. Martin (PDF) * [The Elements of Statistical Learning](https://web.stanford.edu/~hastie/ElemStatLearn/) - Trevor Hastie, Robert Tibshirani, and Jerome Friedman * [The LION Way: Machine Learning plus Intelligent Optimization](https://intelligent-optimization.org/LIONbook/lionbook_3v0.pdf) - Roberto Battiti, Mauro Brunato (PDF) +* [The Mechanics of Machine Learning](https://mlbook.explained.ai) - Terence Parr and Jeremy Howard * [The Python Game Book](http://thepythongamebook.com/en%3Astart) * [Top 10 Machine Learning Algorithms Every Engineer Should Know](https://www.dezyre.com/article/top-10-machine-learning-algorithms/202) - Binny Mathews and Omair Aasim * [Understanding Machine Learning: From Theory to Algorithms](https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning) - Shai Shalev-Shwartz, Shai Ben-David diff --git a/courses/free-courses-en.md b/courses/free-courses-en.md index 85c697aa..766d2358 100644 --- a/courses/free-courses-en.md +++ b/courses/free-courses-en.md @@ -552,6 +552,8 @@ * [Machine Learning Recipes with Josh Gordon](https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal) * [Machine Learning Tutorial Python \| Machine Learning For Beginners](https://www.youtube.com/playlist?list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw) - Dhaval Patel * [Machine Learning with Python by Saeed Aghabozorgi](https://cognitiveclass.ai/courses/machine-learning-with-python) (cognitiveclass.ai) +* [Mathematics for Machine Learning - Linear Algebra](https://www.youtube.com/playlist?list=PLiiljHvN6z1_o1ztXTKWPrShrMrBLo5P3) - Imperial College London, Dr David Dye, Dr Sam Cooper +* [Mathematics for Machine Learning - Multivariate Calclus](https://www.youtube.com/playlist?list=PLiiljHvN6z193BBzS0Ln8NnqQmzimTW23) - Imperial College London, Dr David Dye, Dr Sam Cooper * [Pattern Recognition and Machine Learning](https://www.microsoft.com/en-us/research/people/cmbishop/#!prml-book) * [PyTorch tutorials by PyTorch.org](https://pytorch.org/tutorials) * [Stanford University Machine Learning](https://www.coursera.org/learn/machine-learning) @@ -594,6 +596,7 @@ * [Introduction to Reinforcement Learning with David Silver](https://deepmind.com/learning-resources/-introduction-reinforcement-learning-david-silver) - David Silver * [LouvainX Paradigms of Computer Programming – Abstraction and Concurrency](https://www.edx.org/course/paradigms-computer-programming-louvainx-louv1-2x-1#!) * [LouvainX Paradigms of Computer Programming – Fundamentals](https://www.edx.org/course/paradigms-computer-programming-louvainx-louv1-1x-1) +* [Made with ML](https://madewithml.com) - Goku Mohandas (Applied ML · MLOps · Production) * [MIT 6.S099: Artificial General Intelligence](https://agi.mit.edu) * [MIT Numerical Methods (2014)](http://www.iitg.ernet.in/kartha/CE601-14/CourseSchedule.htm) * [MIT's Artificial Intelligence](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/)