some are from https://github.com/brpy/ml-books
| Book/Resource | Author(s) | Links |
|---|---|---|
| AI | Leonard | [gitbook] |
| d2l-ai | Community | [github] [pdf] |
| Deep Learning with Pytorch | Eli Stevens, Luca Antiga, Thomas Viehmann | [pdf] |
| Ml Primer | Mihail Eric | [pdf] |
| Mathematics For Machine Learning | Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong | [github] [pdf] |
| Foundations of Data Science | Avrim Blum, John Hopcroft, Ravindran Kannan | [pdf] |
| Think Stats | Allen Downey | [github] [pdf] |
| Math4ml | Garrett Thomas | [github] [pdf] |
| Think bayes | Allen Downey | [github] [html] [pdf] |
| Think python 2 | Allen Downey | [pdf] |
| Intermediate python | Muhammad Yasoob Ullah Khalid | [pdf] |
| Pattern Recognition and Machine Learning | Christopher Bishop | [pdf] |
| Computer Age Statistical Inference | Bradley Efron, Trevor Hastie | [pdf] |
| An Introduction to Statistical Learning | Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani | [pdf] |
| The Elements ofStatistical Learning | Trevor Hastie, Robert Tibshirani, Jerome Friedman | [pdf] |
- dynamicdeploy/gninrael-enihcam. A collection of machine learning books, mainly updated in 2017.
- Free machine Learning Books A collection of books from @shahumar
- Awesome machine learning books
| # | Course Provider | URL | Review |
|---|---|---|---|
| 1 | Kaggle | https://www.kaggle.com/learn | From Beginning to Advanced, Kaggle offered many AI courses including basic Python Programming to Deep Learning in very practical way. |
| 2 | Stanford CS229 | http://cs229.stanford.edu/syllabus-autumn2018.html | |
| 3 | IBM Data Science Professional | https://www.coursera.org/professional-certificates/ibm-data-science | On Coursera. This is not free for certificate, but most of course materials are free. |
| 4 | Computer Sciense For AI (edx/HarvardX) | https://www.edx.org/professional-certificate/harvardx-computer-science-for-artifical-intelligence | On Edx |
| ~ | There Are More |