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https://github.com/leandromineti/ml-journey

A curated collection of free learning resources on topics related to Machine Learning.
https://github.com/leandromineti/ml-journey

artificial-intelligence machine-learning mathematics statistics

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A curated collection of free learning resources on topics related to Machine Learning.

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README 

        
# Machine Learning Journey



This repository contains a curated collection of **free** learning resources on topics related to Machine Learning. 

I tend to favor materials that explain concepts visually and promote a hands-on learning experience. 

The references are organized as follows:

- Math & Stats

  - Fundamentals

  - Linear Algebra

  - Calculus

  - Statistics

- Computing

  - Python

  - R

  - SQL

  - NoSQL

  - Git

  - Cloud

- Machine Learning

  - Fundamentals

  - Data-specific resources

  - Model-specific resources

  - Application-specific resources

  - Digging deeper



## Math & Stats



### Fundamentals



- [Khan Academy](https://www.khanacademy.org/) - Fundamental Math courses.

- [Introduction to computational thinking for Data Science - MIT](https://www.youtube.com/playlist?list=PLUl4u3cNGP619EG1wp0kT-7rDE_Az5TNd) - Playlist.

- [Mathematics for Machine Learning - Deisenroth *et al*](https://mml-book.github.io/) - Book.



### Linear Algebra



- [Essence of Linear Algebra - 3Blue1Brown](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) - Playlist.

- [Gilbert Strang lectures on Linear Algebra - MIT](https://www.youtube.com/playlist?list=PL49CF3715CB9EF31D) - Playlist.

- [Introduction to Applied Linear Algebra - Boyd & Vandenberghe](http://vmls-book.stanford.edu/) - Book.



### Calculus



- [Essence of Calculus - 3Blue1Brown](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) - Playlist.

- [The Matrix Calculus You Need For Deep Learning - Explained.ai](https://explained.ai/matrix-calculus/index.html) - Article.



### Statistics



- [Seeing Theory - Brown University](https://seeing-theory.brown.edu/) - Interactive book.

- [Probability Cheatsheet - William Chen](https://github.com/wzchen/probability_cheatsheet) - Cheatsheet.

- [Probabilistic Programming & Bayesian Methods for Hackers - Davidson-Pilon](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/) - Book.

- [Computer Age Statistical Inference - Efron & Hastie](https://web.stanford.edu/~hastie/CASI/) - Book.



## Computing



### Python



- [Learn Python: free interactive python tutorial](https://www.learnpython.org/) - Interactive tutorial.

- [Python programming: learn python in one video - Derek Banas](https://www.youtube.com/watch?v=N4mEzFDjqtA) - Live coding.

- [Python Magical Universe - Anna-Lena Popkes](http://alpopkes.com/portfolio/portfolio-3/) - Interactive course.

- [Reproducible Data Analysis in Jupyter - Jake VanderPlas](http://jakevdp.github.io/blog/2017/03/03/reproducible-data-analysis-in-jupyter/) - Playlist.

- [Microsoft Learn for NASA](https://docs.microsoft.com/en-us/learn/topics/nasa) - Tutorials.



### R



- [fasteR: fast lane to learning R! - Norman Matloff](https://github.com/matloff/fasteR) - Course.

- [R for Data Science - Hadley Wickham](https://r4ds.had.co.nz/) - Book.



### SQL



- [Intro to SQL: Learn SQL for working with databases, using Google BigQuery to scale to massive datasets - Kaggle](https://www.kaggle.com/learn/intro-to-sql) - Interactive tutorial.

- [Advanced SQL: Take your SQL skills to the next level - Kaggle](https://www.kaggle.com/learn/advanced-sql) - Interactive tutorial.

- [The SQL Tutorial for Data Analysis - Mode](https://mode.com/sql-tutorial/introduction-to-sql/) - Tutorial.



### NoSQL



- [MongoDB Basics](https://university.mongodb.com/courses/M001/about) - Course.



### Git



- [The Git Parable](https://tom.preston-werner.com/2009/05/19/the-git-parable.html) - Article.

- [Learn Git branching](https://learngitbranching.js.org/) - Interactive tutorial.

- [Git Internals - Learn by building your own Git](https://www.leshenko.net/p/ugit/) - Interactive tutorial.



### Cloud



- [AWS Cloud Practitioner Essentials](https://www.aws.training/Details/Curriculum?id=27076&scr=path-cp) - Course.



## Machine Learning



### Fundamentals



- [Artificial Intelligence: a concise conceptual introduction - TDS](https://towardsdatascience.com/artificial-intelligence-d1e45efc99b4) - Article.

- [A visual introduction to machine learning I - R2D3](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/) - Article.

- [A visual introduction to machine learning II - R2D3](http://www.r2d3.us/visual-intro-to-machine-learning-part-2/) - Article.

- [Making Friends with Machine Learning - Cassie Kozyrkov](https://www.youtube.com/playlist?list=PLRKtJ4IpxJpDxl0NTvNYQWKCYzHNuy2xG) - Course.

- [Machine Learning Crash Course - Google](https://developers.google.com/machine-learning/crash-course) - Course.

- [Machine Learning for Beginners - Microsoft](https://microsoft.github.io/ML-For-Beginners/#/) - Course.

- [Introduction to Machine Learning for Coders - fast.ai](http://course18.fast.ai/ml) - Course.

- [Practical Deep Learning for Coders - fast.ai](https://course.fast.ai/) - Course.

- [The Elements of Statistical Learning - Hastie *et al*](https://web.stanford.edu/~hastie/ElemStatLearn/) - Book.



### Data-specific resources



#### Natural Language Processing



- [Natural Language Processing: Distinguish yourself by learning to work with text data - Kaggle](https://www.kaggle.com/learn/natural-language-processing) - Interactive tutorial. 

- [A Code-First Introduction to Natural Language Processing - fast.ai](https://www.fast.ai/2019/07/08/fastai-nlp/) - Course.

- [Text as Data - Chris Bail](https://cbail.github.io/textasdata/Text_as_Data.html) - Course.



#### Computer vision



- To do 



#### Time series



- [Forecasting: Principles and Practice - Hyndman & Athanasopoulos](https://otexts.com/fpp2/) - Book.



### Model-specific resources



#### Neural networks



- [Neural Networks - 3Blue1Brown](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) - Playlist.

- [Neural Networks and Deep Learning - Michael Nielsen](http://neuralnetworksanddeeplearning.com/) - Book.

- [Deep Learning from the Foundations - fast.ai](https://course.fast.ai/part2) - Course.



### Application-specific resources



#### Recommender systems



- [How does Netflix recommend movies? Matrix Factorization - Luis Serrano](https://www.youtube.com/watch?v=ZspR5PZemcs) - Video.



### Digging deeper



#### Feature engineering



- [Feature Engineering and Selection: A Practical Approach for Predictive Models - Max Khun](https://bookdown.org/max/FES/) - Book.

- [Feature Engineering: Discover the most effective way to improve your models - Kaggle](https://www.kaggle.com/learn/feature-engineering) - Interactive tutorial.



#### Interpretable Machine Learning



- [Machine Learning Explainability: Extract human-understandable insights from any machine learning model - Kaggle](https://www.kaggle.com/learn/machine-learning-explainability) - Interactive tutorial.

- [Interpretable Machine Learning: A Guide for Making Black Box Models Explainable - Christoph Molnar](https://christophm.github.io/interpretable-ml-book/) - Book.



#### Other cool resources



Newsletters:

- [Data Elixir](https://dataelixir.com/): stay up to date in Data Science.

- [The Batch](https://www.deeplearning.ai/thebatch/): what matters in AI right now.

- [Alpha Signal](https://alphasignal.ai/): the best of Machine Learning. Summarized by AI.



Stranger things:

- [Papers with code](https://www.paperswithcode.com/): reproducible research. Yay!

- [ArXiv Sanity Preserver](http://www.arxiv-sanity.com/): keep your sanity while sifting through ArXiv. The 'top hype' tab is pretty cool.

- [Artificial Intelligence Podcast](https://lexfridman.com/ai/): nice conversations about AI.