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Machine Learning
Use advanced feature engineering strategies and select best features from your data set with a single line of code. Created by Ram Seshadri. Collaborators welcome.
All course materials for the Zero to Mastery Deep Learning with TensorFlow course.
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
Infrastructure to enable deployment of ML models to low-power resource-constrained embedded targets (including microcontrollers and digital signal processors).
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
Github repository for machine learning course owned and maintained by prof. Jahangiry
Extra blocks for scikit-learn pipelines.
🤖⚡ 50 scikit-learn tips
Jupyter notebooks from the scikit-learn video series
Youtube tutorial associated content
Python codes from tutorials on the Data Professor YouTube channel
Additional linear models including instrumental variable and panel data models that are missing from statsmodels.
Statsmodels: statistical modeling and econometrics in Python
A resource for learning about Machine learning & Deep Learning
An open source python library for automated feature engineering
Automated Machine Learning with scikit-learn
Fast, flexible and easy to use probabilistic modelling in Python.
skops is a Python library helping you share your scikit-learn based models and put them in production
PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf
Automatically transform all categorical, date-time, NLP variables to numeric in a single line of code for any data set any size.
Natural Intelligence is still a pretty good idea.
An intuitive library to add plotting functionality to scikit-learn objects.
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning