ml-regression reviews and mentions
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What Is Machine Learning Anyway?
These fields are relatively new. There doesn’t seem to be a clear path to break into these fields.
Personally, I believe Kaggle is one of the ways to slowly gain some practical experience: https://www.kaggle.com/
However, I’m not sure if it’s sufficient.
Recently, I’ve been taking a deeper dive into studying various types of competitions. For example, I’ve created a repo where I’m organizing notebooks, etc for a regression competition:
https://github.com/melling/ml-regression
I’m creating others for classification, nlp, vision, etc
Of course, the self-study method means people have knowledge gaps because there’s no syllabus tailored for an interview
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The Feynman Technique 2.0: How to Take Your Learning to the Next Level
I’ve been studying machine learning for a couple years. Recently I decided to increase my practical knowledge by studying Kaggle notebooks, creating my own, and organizing them on GitHub; making them all public on Kaggle would create a mess IMHO.
I’m not actually planning on teaching the material but I’m trying to organize the material as if I were.
I’ve also created an outline of the topics I want to learn.
This is my Regression repo:
https://github.com/melling/ml-regression
Doing others to study classification, time series, etc.
Stats
melling/ml-regression is an open source project licensed under The Unlicense which is not an OSI approved license.
The primary programming language of ml-regression is Jupyter Notebook.
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