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Learning notes and projects for Machine Learning /Deep Learning

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kevin-zhou-dev/Data_Science_Learning

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1- Learning Ressources

1-a) Courses

Description
Pros - Beginner friendly
- one of the first and best courses in ML
- Theory-driven and in-depth coverage of algorithms
Cons - Taught in Matlab/Octave (more research-oriented language than Python)
- Course made in 2011, introduction-level course to ML
In Details - Useful resources section including informative Websites / ML Datasets / Useful papers
- Assignment and Project solutions
Description
Pros - Taught entirely in Python ( + other ML libraries such as Tensorflow or Keras)
- Course made in 2017-2018
Cons
In Details Assignment and Project solutions

1-b) Ebooks

1-c) Research Papers

1-d) GitHub Repo

amitness/learning - github repo containing a quite exhaustive list of online courses + e-books ossu/data-science - github repo EbookFoundation/free-programming-books - github repo rushter/data-science-blogs: github repo of lists of data science blogs vinta/awesome-python: curated list of Python frameworks, libraries, ressources...

1-e) Good articles

Data skeptic on spotify including
-Podcast of entry level : MINI series
-Podcast of intermediate to advanced level : interviews with ML experts

2- Interview preparation

2-a) Cheat sheets

2-b) Coding interviews

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