The ability to write implementations of machine learning algorithms in pure C allows developers to very efficiently manage memory allocation, concurrency, and control flow. That means fast implementations that can outperform preexisting models in other languages, including even (gasp) Python. It’s a useful skill to know and in this live stream I’ll use C and C-based Python tools like Cython + spaCy to develop some really fast natural language processing algorithms for text data. We’ll be able to tokenize, tag, normalize, vectorize, and dependency parse articles of text to derive valuable insights. No installation necessary, we'll do this together using Google Colab in the browser. Join me, there’s a lot to cover here!

Code for this video:
https://github.com/llSourcell/c_programming_for_machine_learning

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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More learning resources:
https://pydata.org/berlin2016/schedule/presentation/51/
https://smerity.com/articles/2018/cython_for_high_and_low.html
https://explosion.ai/blog/writing-c-in-cython
https://spacy.io/api/cython
https://medium.com/huggingface/100-times-faster-natural-language-processing-in-python-ee32033bdced

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