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# System prepended metadata

title: Road Map to Machine Learning

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# Road Map to Machine Learning

## Getting into AI 

Today there are a plenty of tutorials, blogs, videos, courses, memes, college degrees in the market to get you started with the recent trends and concepts. 
Among all this clutter, we've got you covered with a few trustworthy set of resources that we're personally a fan of and always fall back to:

## Join the community 

- Follow these people on Twitter
    - [@AlphaSignalAI](https://twitter.com/alphasignalai)
    - [@karpathy](https://twitter.com/karpathy)

- Join these subreddits 

    - [r/deeplearning](https://www.reddit.com/r/deeplearning/)
    - [r/datascience](https://www.reddit.com/r/datascience/)
    - [r/learnmachinelearning](https://www.reddit.com/r/learnmachinelearning/)
    - [r/ProgrammerHumor](https://www.reddit.com/r/ProgrammerHumor/)

- Follow these instagram handles 
    - [@machinelearning](https://www.instagram.com/machinelearning/)

- Subscribe to these newsletters 
    - [AI Summer](https://theaisummer.com/)
    - [Microsoft Research Newsletter](https://www.microsoft.com/en-us/research/blog/)

## Further Resources

The field of machine learning is vast and has aspects varying from pure mathematics to software development. But one must get a basic idea of each aspect before moving forward. Resources listed below are not to be followed in a strictly linear fashion, choose to begin with what intrigues you. It's also advisable for anyone who chooses to follow this roadmap to not spend too much time on one resource before moving on to another, these resources can be treated simply as reference materials rather than deep diving fully into one. 

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### Maths for AI
Essentials
- [Essence of linear algebra by 3Blue1Brown](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)
- [Stats 110](https://projects.iq.harvard.edu/stat110/home)
- [What is Automatic Differentiation?](https://www.youtube.com/watch?v=wG_nF1awSSY)

Advanced (optional):
- [Gilbert Strang lectures on Linear Algebra (MIT)](youtube.com/playlist?list=PL49CF3715CB9EF31D)
- [Mathematics for Machine Learning](https://mml-book.github.io/book/mml-book.pdf)

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### Basic Coding Skills

- [CS50 2022 - Lecture 6 - Python](https://www.youtube.com/watch?v=5Jppcxc1Qzc)
- [python-course](https://python-course.eu/python-tutorial/l)

Implementation Practice 

- [From Python to NumPy](https://www.labri.fr/perso/nrougier/from-python-to-numpy/)

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### Machine Learning
Essentials
- [Stanford CS229: Machine Learning Full Course](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU)

Advanced (optional):
- Kilian Weinberger's [Cornell CS47580 class](https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS) and [notes](https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/)
- [An Introduction to Statistical Learning](https://www.statlearning.com/)

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### Deep Learning

Essentials
- [Neural Networks by 3Blue1Brown](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)
- [Neural Networks: Zero to Hero](https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ)
- [Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning)

Advanced (optional):
- [UMass CS685: Advanced Natural Language Processing](https://www.youtube.com/playlist?list=PLWnsVgP6CzadmQX6qevbar3_vDBioWHJL)
- [Deep Learning for Computer Vision](https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r)
- [Introduction to PyTorch](https://pytorch.org/tutorials/beginner/introyt.html) (Our framework of choice for building Deep Learning applications)
- [Dive into Deep Learning](https://d2l.ai/) (learn theory via implementations)

