Awesome AI

This repo, has a curated list of all things that we have done and the courses we've passed during learning ML and DL. if you think there is a course or something else that there isn't in this repo, feel free to contribute and add them :)
So if you want to contribute, please first check the contribution section.

Courses

Machine Learning

• Machine Learning - University of Stanford - Andrew NG (🔗)
  • implementation repo (🔗)
• Machine Learning - University of Washington - Emily Fox, Carlos Guestrin - Specialization (🔗)
  • implementation repo (🔗)
• Mathematics for Machine Learning - Imperial College London - David Dye, Samuel J. Cooper, A. Freddie Page, Marc Peter Deisenroth - Specialization (🔗)
• SYDE 522 – Machine Intelligence (Winter 2019) - University of Waterloo - H.R.Tizhoosh (🔗)

Deep Learning

• Deep Learning - DeepLearning.AI - Andrew NG, Kian Katanforoosh, Younes Bensouda Mourri - Specialization (🔗)

Natural Language Processing

• CS224n: Natural Language Processing with Deep Learning - University of Stanford - Chris Manning (🔗) (📼)

Speech

Computer Vision

Reinforcement Learning

• Reinforcement Learning Specialization - University of Alberta - Martha White, Adam White (🔗)
• Introduction to Reinforcement Learning 2015 - DeepMind x University College London - David Silver (🔗)

Datasets

Natural Language Processing

PersianQuAD: The Native Question Answering Dataset for the Persian Language

Question-Answering PersianQuAD contains approximately 20,000 "question, paragraph, answer" triplets on Persian Wikipedia articles and is the first large-scale native QA dataset for the Persian language which is created by native annotators.
(🔗)

Computer Vision

Speech

Repos

• ML YouTube Courses | readme | (🔗)
• YSDA Natural Language Processing course | nlp notebooks hands-on | (🔗)
• Class.Vision | vision notebooks hands-on | (🔗)
• Deep Learning Models | vision pre-trained models Keras deprecated | (🔗)

Youtube Videos/Channels/Playlists

• 3Blue1Brown | calculus | (🔗)
• StatQuest with Josh Starmer | Statistics ML | (🔗)
• Steve Brunton | RL Linear Algebra | (🔗)
• Yannic Kilcher | ML DL Paper Review | (🔗)
• Valerio Velardo - The Sound of AI | Speech | (🔗)

Articles and Papers

• Neural Networks and Deep Learning - Michael Nielsen - Online Book free (🔗)
• Learning Internal Representations by Error Propagation - Rumelhart, D. E., Hinton, G. E., Williams, R. J. - paper free (🔗)

Contribution

Let's Complete this awesome readme :)
Cause the lack of data and resources in this readme, we appreciate the new links and resources you introduce to us. Together we can do great jobs! so just don't hesitate and start your contribution.

Contribution is so simple. There are two ways:

1. open an issue and tell us about the resource
2. fork this repo and then add your desired resources (note that you have to follow the current format of text otherwise your PR will be rejected)

It's really easy, isn't it?