Guidebooks are helpful to store knowledge that you might use later on. They are used everywhere in the world.. so why not machine learning? There are a ton of helpful resources out there on sites like coursera, udemy, research papers, ml books but sometimes we just want that go to source.
The intent of this repo is to provide a source of truth when approaching an ml problem with data you are not familiar with. Feel free to contribute using a python notebook, as well as contributing to the various notes in this repository. If you see something thats not correct or a good tip that could help me or someone else open a PR and we'll get it in there.
Compliling notes for predicition analytics. Feel free to contribute
- What to do with a dataset you have never seen before?
- What to look for in new datasets?
- etc..
- scikit-learn
- tensorflow
- python3
- seaborn visualizations
- Linear-Regression
- Feel free to contrubute a model.