tpot - A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming

•    Python

---

Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data.

machine-learning data-science automl automation scikit-learn hyperparameter-optimization model-selection parameter-tuning automated-machine-learning random-forest gradient-boosting feature-engineering xgboost genetic-programming

TransmogrifAI - TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Spark with minimal hand tuning

•    Scala

---

TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library written in Scala that runs on top of Spark. It was developed with a focus on accelerating machine learning developer productivity through machine learning automation, and an API that enforces compile-time type-safety, modularity, and reuse. Through automation, it achieves accuracies close to hand-tuned models with almost 100x reduction in time. Skip to Quick Start and Documentation.

ml automl transformations estimators dsl pipelines machine-learning salesforce einstein features feature-engineering spark sparkml ai automated-machine-learning transmogrification transmogrify structured-data transformers

featuretools - automated feature engineering

•    Python

---

Featuretools is a python library for automated feature engineering. See the documentation for more information. Below is an example of using Deep Feature Synthesis (DFS) to perform automated feature engineering. In this example, we apply DFS to a multi-table dataset consisting of timestamped customer transactions.

feature-engineering machine-learning data-science automated-machine-learning automl scikit-learn automated-feature-engineering

xcessiv - A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python

•    Python

---

Stacked ensembles are simple in theory. You combine the predictions of smaller models and feed those into another model. However, in practice, implementing them can be a major headache. Xcessiv holds your hand through all the implementation details of creating and optimizing stacked ensembles so you're free to fully define only the things you care about.

machine-learning ensemble-learning stacked-ensembles scikit-learn data-science hyperparameter-optimization automated-machine-learning

auto-sklearn - Automated Machine Learning with scikit-learn

•    Python

---

auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.

automl scikit-learn automated-machine-learning hyperparameter-optimization hyperparameter-tuning hyperparameter-search bayesian-optimization metalearning meta-learning smac

auto_ml - Automated machine learning for analytics & production

•    Python

---

auto_ml is designed for production. Here's an example that includes serializing and loading the trained model, then getting predictions on single dictionaries, roughly the process you'd likely follow to deploy the trained model. All of these projects are ready for production. These projects all have prediction time in the 1 millisecond range for a single prediction, and are able to be serialized to disk and loaded into a new environment after training.

machine-learning data-science automated-machine-learning gradient-boosting scikit-learn machine-learning-pipelines machine-learning-library production-ready automl lightgbm analytics feature-engineering hyperparameter-optimization deep-learning xgboost keras deeplearning tensorflow artificial-intelligence

featuretools - An open source python library for automated feature engineering

•    Python

---

Featuretools is a python library for automated feature engineering. See the documentation for more information.

data-science machine-learning scikit-learn feature-engineering automl automated-machine-learning automated-feature-engineering

autokeras - AutoML library for deep learning

•    Python

---

AutoKeras: An AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University. The goal of AutoKeras is to make machine learning accessible to everyone. Official website tutorials.

machine-learning deep-learning tensorflow keras automl automated-machine-learning neural-architecture-search autodl

machineJS - Automated machine learning- just give it a data file! Check out the production-ready version of this project at ClimbsRocks/auto_ml

•    Python

---

I just built out v2 of this project that now gives you analytics info from your models, and is production-ready. machineJS is an amazing research project that clearly proved there's a hunger for automated machine learning. auto_ml tackles this exact same goal, but with more features, cleaner code, and the ability to be copy/pasted into production.

machine-learning data-science machine-learning-library machine-learning-algorithms ml data-scientists javascript-library scikit-learn kaggle numerai automated-machine-learning automl auto-ml neuralnet neural-network algorithms random-forest svm naive-bayes bagging optimization brainjs date-night sklearn ensemble data-formatting js xgboost scikit-neuralnetwork knn k-nearest-neighbors gridsearch gridsearchcv grid-search randomizedsearchcv preprocessing data-formatter kaggle-competition

SMAC3 - Sequential Model-based Algorithm Configuration

•    Python

---

Attention: This package is under heavy development and subject to change. A stable release of SMAC (v2) in Java can be found here. The documentation can be found here.

bayesian-optimization bayesian-optimisation hyperparameter-optimization hyperparameter-tuning hyperparameter-search configuration algorithm-configuration automl automated-machine-learning

conveyer - Machine Learning for everybody.

•    Python

---

Machine Learning for everybody. conveyer is an automated machine learning library. Free for personal use only. Contact @yusugomori for commercial use or more details.

automated-machine-learning machine-learning python3

data-formatter - Takes raw csv input and formats it to be ready for neural networks

•    Python

---

Formats and cleans your data to get it ready for machine learning!

neural-network machine-learning data-formatting normalization min-max-normalization min-max-normalizing brain.js automated-machine-learning bestbrain data-science kaggle scikit-learn sklearn scikit-neuralnetworks lasagne nolearn nolearn.lasagne data-cleaning data-munging data-preparation imputing-missing-values filling-in-missing-values dataset data-set training testing random-forest vectorization categorization one-hot-encoding dictvectorizer preprocessing feature-selection feature-engineering

empythy - Automated NLP sentiment predictions- batteries included, or use your own data

•    Python

---

The classic sentiment corpus, 2000 movie reviews already gathered by NLTK. CrowdFlower hosts a number of Twitter corpora that have already been graded for sentiment by panels of humans.

nlp-sentiment-classifier nlp nlp-machine-learning nlp-library sentiment-classification sentiment-predictions sentiment-classifier sentiment machine-learning machinelearning batteries-included automated-machine-learning

FEDOT - Automated modeling and machine learning framework FEDOT

•    Python

---

This repository contains FEDOT - an open-source framework for automated modeling and machine learning (AutoML). It can build custom modeling pipelines for different real-world processes in an automated way using an evolutionary approach. FEDOT supports classification (binary and multiclass), regression, clustering, and time series prediction tasks. The main feature of the framework is the complex management of interactions between various blocks of pipelines. First of all, this includes the stage of machine learning model design. FEDOT allows you to not just choose the best type of the model, but to create a complex (composite) model. It allows you to combine several models of different complexity, which helps you to achieve better modeling quality than when using any of these models separately. Within the framework, we describe composite models in the form of a graph defining the connections between data preprocessing blocks and model blocks.

machine-learning automation genetic-programming hyperparameter-optimization evolutionary-algorithms multimodality automl automated-machine-learning parameter-tuning structural-learning

auptimizer - An automatic ML model optimization tool.

•    Python

---

Getting the best models in minimum time - Generate optimal models and achieve better performance by employing state-of-the-art hyperparameter optimization (HPO) and model compression techniques. Auptimizer will run and record sophisticated HPO and model compression experiments on compute resources of your choice with effortless consistency and reproducibility. Making your models edge-ready - Get model-device compatibility and enhanced on-device performance by converting models into the industry-standard ONNX and TensorFlow Lite formats. Auptimizer-Converter provides validated conversion techniques to ensure worry-free format transformations.

data-science machine-learning deep-learning data-engineering neural-networks hyperparameter-optimization hyperparameter-tuning hpo automl automated-machine-learning