Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/VidyasagarMSC/Awesome-AI
The guide to master Artificial Intelligence (machine learning & deep learning) from beginner to advance
https://github.com/VidyasagarMSC/Awesome-AI
List: Awesome-AI
article artificial-intelligence cheatsheet deep-learning infographics machine-learning machine-learning-algorithms mooc open-source tutorial
Last synced: 2 months ago
JSON representation
The guide to master Artificial Intelligence (machine learning & deep learning) from beginner to advance
- Host: GitHub
- URL: https://github.com/VidyasagarMSC/Awesome-AI
- Owner: VidyasagarMSC
- License: mit
- Created: 2018-05-07T15:54:45.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-03-31T20:18:26.000Z (10 months ago)
- Last Synced: 2024-10-27T18:17:04.644Z (3 months ago)
- Topics: article, artificial-intelligence, cheatsheet, deep-learning, infographics, machine-learning, machine-learning-algorithms, mooc, open-source, tutorial
- Homepage:
- Size: 787 KB
- Stars: 145
- Watchers: 16
- Forks: 30
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-artificial-intelligence - Awesome-AI - The guide to master Artificial Intelligence (machine learning & deep learning) from beginner to advance. (Awesome lists for learning AI)
- ultimate-awesome - Awesome-AI - The guide to master Artificial Intelligence (machine learning & deep learning) from beginner to advance. (Other Lists / Monkey C Lists)
README
# Awesome-AI
A curated list of articles, books, courses, infographics and many more covering Artificial Intelligence, Machine Learning and Deep Learning.
> Check the new [**Awesome-DS**](https://github.com/VidyasagarMSC/Awesome-DS) repo for Data Science content
## Beginners - Getting Started
### :pencil: Articles
- [An introduction to Artificial Intelligence](https://hackernoon.com/understanding-understanding-an-intro-to-artificial-intelligence-be76c5ec4d2e)
- [A beginner's guide to artificial intelligence, machine learning, and cognitive computing](https://developer.ibm.com/articles/cc-beginner-guide-machine-learning-ai-cognitive/)
- [Which machine learning algorithm should I use?](https://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/?utm_source=facebook&utm_medium=cpc&utm_campaign=analytics-global&utm_content=US_interests-conversions)
- [The Journey of a Machine Learning model from Building to Retraining](https://towardsdatascience.com/the-journey-of-a-machine-learning-model-from-building-to-retraining-fe3a37c32307?gi=38d2b73db825)
- [An executive’s guide to AI by McKinsey&Company](https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/an-executives-guide-to-ai)
- [A Comprehensive Guide On How to Monitor Your Models in Production](https://neptune.ai/blog/how-to-monitor-your-models-in-production-guide)### :page_facing_up:Courses
- [Introduction to Artificial Intelligence by Microsoft on edx](https://www.edx.org/course/introduction-to-artificial-intelligence-ai)
- [Introduction to Machine Learning crash course by Google](https://developers.google.com/machine-learning/crash-course/ml-intro)
- [Foundations of Machine Learning(foml) by Bloomberg](https://www.techatbloomberg.com/foml)### :books:Books
- [5 books to Master Machine Learning](https://www.kdnuggets.com/5-free-books-to-master-machine-learning)
- [Machine Learning Yearning by Andrew Ng - Signup for a free draft copy](http://www.mlyearning.org) - *Approx. 100 pages*
- [The Hundred-Page Machine Learning Book](http://themlbook.com/wiki/doku.php)### :snowman:Quick guides
- [Introductory Guide to Artificial Intelligence](https://towardsdatascience.com/introductory-guide-to-artificial-intelligence-11fc04cea042)
- [DZone's Guide to
Artificial Intelligence: Machine Learning and Predictive Analytics](https://dzone.com/guides/artificial-intelligence-machine-learning-and-predi)
- [What is Machine Learning?](https://www.mathworks.com/content/dam/mathworks/tag-team/Objects/i/88174_92991v00_machine_learning_section1_ebook.pdf)
- [Introducing Deep Learning with MATLAB](https://es.mathworks.com/content/dam/mathworks/tag-team/Objects/d/80879v00_Deep_Learning_ebook.pdf)
- [Introduction to Tensorflow](https://dzone.com/refcardz/introduction-to-tensorflow?chapter=1)### Cheatsheets
- [AI cheatsheets](https://www.codecademy.com/resources/cheatsheets/subject/artificial-intelligence)
- [The machine learning algorithm cheat sheet - sas.com](cheatsheets/machinelearning/machine-learning-cheat-sheet-sas.png)
- [Machine Learning Cheatsheet](http://ml-cheatsheet.readthedocs.io/en/latest/index.html)
- Stanford CS 229:
- Deep Learning: http://stanford.io/2BsQ91Q
- Supervised Learning: http://stanford.io/2nRlxxp
- Unsupervised Learning: http://stanford.io/2MmP6FN
- [Machine Learning for dummies cheat sheet](https://www.dummies.com/programming/big-data/data-science/machine-learning-dummies-cheat-sheet/)### Tutorials
- [Machine Learning Tutorial for Beginners – Learn Machine Learning](https://data-flair.training/blogs/machine-learning-tutorial/)
- [Deep Learning Tutorial for Beginners - Kaggle](https://www.kaggle.com/kanncaa1/deep-learning-tutorial-for-beginners)### Presentations
- [Deep Learning - The Past, Present and Future of Artificial Intelligence](https://www.slideshare.net/LuMa921/deep-learning-the-past-present-and-future-of-artificial-intelligence)## :robot:Advanced AI
### :page_facing_up:Courses
- [Deeplearning.ai | Coursera by Andrew Ng](https://www.deeplearning.ai) - *5 courses*
- [Machine Learning | Coursera by Andrew Ng](https://www.coursera.org/learn/machine-learning)
- [fast.ai](http://www.fast.ai) courses
- Deep Learning Part 1: [Practical Deep Learning for Coders](http://course.fast.ai/lessons/lessons.html)
- Deep Learning Part 2: [Cutting Edge Deep Learning for Coders](http://course.fast.ai/part2.html)### :books: Books
- [Deep Learning, An MIT Press book](http://www.deeplearningbook.org)
- [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf)## Infographics
- [AI "Technology Readiness" Infographic](infographics/AI-tech-landscape-graphic.png)
*Source: CallaghanInnovation*
- [AI Timeline](infographics/AI-Timeline.jpg) *Source: Apttus*
- [AI detailed Timeline](infographics/Artificial-Intelligence-AI-Timeline-Infographic.jpeg) *Source: Digital Intelligence Today*## Opensource Libraries and Tools
- [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) - Gymnasium(formerly Gym) is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pong or Pinball.
- [TensorFlow](https://www.tensorflow.org) - An open source machine learning framework for everyone
- [Explore AI Libraries](https://kandi.openweaver.com/explore/artificial-intelligence) - Discover & find a curated list of AI popular & new libraries, top authors, trending project kits, discussions, tutorials & learning resources on kandi.## Interactive Tutorials
- [Seedbank](http://tools.google.com/seedbank/) - Collection of Interactive Machine Learning Examples
- [R2D3](http://www.r2d3.us) - A visual introduction to machine learning## Tips and Tricks
- [Stanford CS 229](http://stanford.io/2MEHwFM)
## Free books collection
- [Engati blog](https://www.engati.com/blog/best-artificial-intelligence-books)## Datasets
- [For benchmarking Deep Learning algorithms](http://deeplearning.net/datasets/)
> Thank and show your :hearts: to the respective authors
:exclamation: *If you see a broken link, open an [issue](https://github.com/VidyasagarMSC/Awesome-AI/issues/new).*
#### Fork this repo, add more content and a PR to merge