Predictive analytics - machine learning is when a lot of data is used to predict some other data by finding which parts affect which other parts. The goal is to reduce RMSE and that is a pretty simple metric. There used to be a time when a data scientist had to munge wrangle data, create generate and select features, select models and hyperparameters and maybe even understand the data. Not anymore. Now the system just runs its self and out pops the best possible answer with less interpret-ability than there ever used to be.
Python is an interpreted, interactive, object-oriented, extensible programming language. It provides an extraordinary combination of clarity and versatility, and is free and comprehensively ported.
R is a free software environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be consider.
github is a better wikipedia https://github.com/hibayesian/awesome-automl-papers
DataRobot's automated machine learning platform makes it fast and easy to build and deploy accurate predictive models. Learn how you can become an AI-driven enterprise today.
Cloud AutoML helps you easily train high quality custom machine learning models with limited machine learning expertise needed.
short history https://blog.datarobot.com/automated-machine-learning-short-history
H2O is an open source, in-memory, distributed, fast, and scalable machine learning and predictive analytics platform that allows you to build machine learning models on big data and provides easy productionalization of those models in enterprise env.
The ML Studio is interactive for EDA, statistical modeling and machine learning applications. Based on Shiny, with Plotly interactive data visualization, DT HTML tables and H2O machine learning and deep. data science pipeline workflow.
mlr provides this so that you can focus on your experiments! The framework provides supervised methods like classification, regression and survival analysis along with their corresponding evaluation and optimization methods, as well as unsupervised methods like clustering.
Consider caret ensemble. https://moderntoolmaking.blogspot.com/2013/03/new-package-for-ensembling-r-models.html
"Predict, intelligently manage, interpret behaviors, automate, Prevision.io brings artificial intelligence into your business at an affordable cost and unparalleled performance."
RapidMiner makes data science teams more productive through an open source platform for data prep, machine learning, and model deployment.
hi http://www.ml4aad.org/automl/
SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market.
The IBM SPSS software platform offers advanced statistical analysis, a vast library of machine-learning algorithms, text analysis, open-source extensibility, integration with big data and seamless deployment into applications.
Amazon Machine Learning allows developers to use machine learning. It provides visualization tools and wizards that guide you in the process of creating machine learning (ML) models. It makes it easy to obtain predictions using simple APIs.
BigML's goal is to create a machine learning service extremely easy to use and seamless to integrate.
Algorithmia makes applications smarter, by building a community around algorithm development, where state of the art algorithms are always live and accessible to anyone.
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really only https://github.com/EpistasisLab/tpot https://github.com/automl/auto-sklearn and maybe https://github.com/HDI-Project/ATM https://github.com/rmcantin/bayesopt
just in case http://www.ml4aad.org/automl/
don't forget https://www.automaticstatistician.com/index/
and ez kaggles:https://mljar.com/