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List of interesting links about ML Algorithms, Data Science, Network Analysis, and others.

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# Awesome ML
List of interesting things about Machine Learning Algorithms:

## [AI Blogs](https://github.com/PetroIvaniuk/awesome-ml/blob/master/Blogs.md) ##

## [Linear Algebra](https://github.com/PetroIvaniuk/awesome-ml/blob/master/Linear%20Algebra.md) ##

## [Natural Language Processing (NLP)](https://github.com/PetroIvaniuk/awesome-ml/blob/master/nlp.md) ##

## [Network Analysis](https://github.com/PetroIvaniuk/awesome-ml/blob/master/Network%20Analysis.md) ##

## [GIS](https://github.com/PetroIvaniuk/awesome-ml/blob/master/GIS.md) ##

## [Reinforcement Learning](https://github.com/PetroIvaniuk/awesome-ml/blob/master/Reinforcement%20Learning.md) ##

## Courses ##

- [CS50’s Introduction to Artificial Intelligence with Python](https://cs50.harvard.edu/ai/2020/) by Brian Yu and at David J. Malan at Harvard
- Applied Machine Learning (CS5785) at Cornell Tech by [Volodymyr Kuleshov](https://www.cs.cornell.edu/~kuleshov/). [Video Fall 2020](https://www.youtube.com/playlist?list=PL2UML_KCiC0UlY7iCQDSiGDMovaupqc83), [Github Fall 2020](https://github.com/kuleshov/cornell-cs5785-2020-applied-ml), [Github Fall 2021](https://github.com/kuleshov/cornell-cs5785-2021-applied-ml/tree/main/notebooks)
- [CS 228 - Probabilistic Graphical Models](https://ermongroup.github.io/cs228-notes/) by Volodymyr Kuleshov and Stefano Ermon
- Making Friends with Machine Learning by Cassie Kozyrkov. [Video Yotube](https://www.youtube.com/playlist?list=PLRKtJ4IpxJpDxl0NTvNYQWKCYzHNuy2xG)
- [CS 329S: Machine Learning Systems Design](https://stanford-cs329s.github.io/index.html) by Stanford
    * [Winter 2021 Schedule & syllabus](https://stanford-cs329s.github.io/2021/syllabus.html)
- [Full Stack Deep Learning](https://fullstackdeeplearning.com/) by [Josh Tobin](http://josh-tobin.com/) and [Sergey Karayev](https://sergeykarayev.com/) and [Pieter Abbeel](https://people.eecs.berkeley.edu/~pabbeel/)
    * [2022](https://fullstackdeeplearning.com/course/2022/), [2021](https://fullstackdeeplearning.com/spring2021/), [Youtube](https://www.youtube.com/channel/UCVchfoB65aVtQiDITbGq2LQ)
- Deep Learning by NYU
    * [Spring 2020](https://atcold.github.io/pytorch-Deep-Learning/)
- [Introduction to Deep Learning](http://introtodeeplearning.com/index.html) by MIT. [Video](https://youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI)
- [MIT Deep Learning and Artificial Intelligence Lectures](https://deeplearning.mit.edu/) by [Lex Fridman](https://lexfridman.com/) and others at MIT
- [Practical Deep Learning for Coders](https://course.fast.ai/) by FastAI
- [Neural Networks from Scratch in Python](https://www.youtube.com/playlist?list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3) by Sentdex

## Books ##

- [Mathematics for Machine Learning](https://mml-book.github.io/) by [Deisenroth](https://deisenroth.cc/), A. Aldo Faisal, and Cheng Soon Ong
- [Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book/) by Christoph Molnar
- [The Mechanics of Machine Learning](https://mlbook.explained.ai/) by Terence Parr and Jeremy Howard
- [Forecasting: Principles and Practice (2nd ed)](https://otexts.com/fpp2/) by Rob J Hyndman and George Athanasopoulos
- [Dive into Deep Learning](https://d2l.ai/) by Alex Smola and other
- [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/) by Michael Nielsen 
- [Deep Learning](https://www.deeplearningbook.org/) by Ian Goodfellow and Yoshua Bengio and Aaron Courville
- [Algorithms & Data Structures](https://superstudy.guide/algorithms-data-structures/foundations/algorithmic-concepts) by Afshine Amidi and Shervine Amidi
- Case Studies in Neural Data Analysis by Mark Kramer and Uri Eden. [The material in Python](https://mark-kramer.github.io/Case-Studies-Python/intro.html) by NVIDIA

## Challenges ##

- [Kaggle](https://www.kaggle.com/) is data science challenges platform.
 - [Xeek](https://xeek.ai/challenges) is challenges unite the data and geoscience communities around the shared goal of crowdsourcing innovative solutions that solve the biggest challenges in exploration.
 - [Alexa Prize](https://developer.amazon.com/alexaprize)
 - [Data-Centric AI Competition](https://https-deeplearning-ai.github.io/data-centric-comp/) by Andrew Ng.
 - [The Animal-AI Testbed](http://www.animalaiolympics.com/AAI/) 
 - [Waymo Open Dataset Challenges](https://waymo.com/open/challenges/): Motion Prediction, Interaction Prediction, Real-Time 2D Detection, Real-Time 3D Detection.

## Learning Platforms ##
 
- [Coursera](https://www.coursera.org/)
- [EDX](https://www.edx.org/)
- [Datacamp](https://learn.datacamp.com/)
- [Prometeus](https://prometheus.org.ua/)
- [Stepik](https://stepik.org/)
- [HackerRank](https://www.hackerrank.com/) - practice coding skills
- [PythonProgramming](https://pythonprogramming.net/)

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