If you didn't have "formal" training in #machinelearning and you are at a point where you can use some models to do "things", but you'd like to understand more about it, my suggestion for best book is: https://www.cambridge.org/core/books/machine-learning/621D3E616DF879E494B094CC93ED36A4
When evaluating a #machinelearning/#datascience problem always account time for every step:
- framing the problem
- defining data sources
- Data analysis
If you account only modeling you're saying: "Building this app took 30 minutes!" when that is just the compile/build time
Here you can find a thorough tutorial on how to use my library (clj-boost) for XGBoost https://www.rdisorder.eu/2018/12/03/machine-learning-clojure-xgboost/
But now the same functionality - and much more - is achievable with https://github.com/generateme/fastmath
Please, if you can try these libraries and let us know what you think about them!!! Anything would be really helpful!
I build data products, some call me data scientist, some data engineer, some software developer.
This Mastodon instance is for people interested in technology. Discussions aren't limited to technology, because tech folks shouldn't be limited to technology either!