josephmisiti/awesome-machine-learning
1
1
Fork 0
mirror of https://github.com/josephmisiti/awesome-machine-learning.git synced 2024-11-23 21:23:37 +03:00
Code Issues Packages Projects Releases Wiki Activity

Merge pull request #1 from nkconnor/nkconnor-remove-auto_ml

Browse Source 
Remove auto_ml Python library
This commit is contained in:
Nicholas Connor 2017-02-23 17:43:53 -07:00 committed by GitHub
commit abe5ec8bca
1 changed files with 0 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
README.md
View File

@ -762,7 +762,6 @@ on MNIST digits[DEEP LEARNING]
<a name="python-general-purpose" />
#### General-Purpose Machine Learning
* [auto_ml](https://github.com/ClimbsRocks/auto_ml) - Automated machine learning pipelines for analytics and production. Handles some standard feature engineering, feature selection, model selection, model tuning, ensembling, and advanced scoring, in addition to logging output for analysts trying to understand their datasets.
* [machine learning](https://github.com/jeff1evesque/machine-learning) - automated build consisting of a [web-interface](https://github.com/jeff1evesque/machine-learning#web-interface), and set of [programmatic-interface](https://github.com/jeff1evesque/machine-learning#programmatic-interface) API, for support vector machines. Corresponding dataset(s) are stored into a SQL database, then generated model(s) used for prediction(s), are stored into a NoSQL datastore.
* [XGBoost](https://github.com/dmlc/xgboost) - Python bindings for eXtreme Gradient Boosting (Tree) Library
* [Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) - Book/iPython notebooks on Probabilistic Programming in Python