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https://github.com/PetroIvaniuk/awesome-ml

List of interesting links about ML Algorithms, Data Science, Network Analysis, and others.
https://github.com/PetroIvaniuk/awesome-ml

List: awesome-ml

data-science deep-learning large-language-models llm machine-learning

Last synced: 3 months ago
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List of interesting links about ML Algorithms, Data Science, Network Analysis, and others.

Host: GitHub
URL: https://github.com/PetroIvaniuk/awesome-ml
Owner: PetroIvaniuk
Created: 2019-03-07T15:58:52.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2023-05-09T18:12:25.000Z (over 1 year ago)
Last Synced: 2024-04-18T14:38:30.609Z (7 months ago)
Topics: data-science, deep-learning, large-language-models, llm, machine-learning
Homepage:
Size: 236 KB
Stars: 10
Watchers: 3
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

ultimate-awesome - awesome-ml - List of interesting links about ML Algorithms, Data Science, Network Analysis, and others. (Other Lists / PowerShell Lists)

README

        # 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/)