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.
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 considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.
R is a whole language with its working bundled application as specially the "de facto" standard for data analysis and data mining. Better suited for advanced users who want all the power in their hands.
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.
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.
The healthcare industry has massive amounts of data available in health records, clinical trials, and billings and claims processing systems; and yet, the industry still struggles to unlock value in this data to drive better patient outcomes and comply with healthcare regulations.
Automated machine learning is helping transform the billions of data points collected in electronic health records, clinical trials, and billings and claims processing into predictions that drive down costs, improve operations, and ultimately, save lives.
Cloud AutoML helps you easily train high quality custom machine learning models with limited machine learning expertise needed.
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. It is written in a way that you can extend it yourself or deviate from the implemented convenience methods and your own complex experiments.
package is nicely connected to the OpenML R package , which aims at supporting collaborative machine learning online and allows to easily share datasets as well as machine learning tasks, algorithms and experiments.
Clear S3 interface to R classification, regression, clustering and survival analysis methods
Possibility to fit, predict, evaluate and resample models
Easy extension mechanism through S3 inheritance
Abstract description of learners and tasks by properties
Parameter system for learners to encode data types and constraints
Many convenience methods and generic building blocks for your machine learning experiments
Resampling methods like bootstrapping, cross-validation and subsampling
Extensive visualizations for e.g. ROC curves, predictions and partial predictions
Benchmarking of learners for multiple data sets
Easy hyperparameter tuning using different optimization strategies, including potent configurators like iterated F-racing (irace) or sequential model-based optimization
Variable selection with filters and wrappers
Nested resampling of models with tuning and feature selection
Cost-sensitive learning, threshold tuning and imbalance correction
Wrapper mechanism to extend learner functionality in complex and custom ways
Combine different processing steps to a complex data mining chain that can be jointly optimized
OpenML connector for the Open Machine Learning server
Extension points to integrate your own stuff
Parallelization is built-in
Consider caret ensemble. https://moderntoolmaking.blogspot.com/2013/03/new-package-for-ensembling-r-models.html
Weka is a collection of machine learning algorithms for data mining tasks; with its own GUI.
(The application is named after a flightless bird of New Zealand that is very inquisitive.)
The algorithms can either be applied directly to a dataset or called from your own Java code.
Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
"Predict, intelligently manage, interpret behaviors, automate, Prevision.io brings artificial intelligence into your business at an affordable cost and unparalleled performance."
Ensure a perfectly reliable electricity distribution nationwide, predict demand and production or control energy consumption.
Manage relationships with millions of customers, portfolio of hundreds of products, and predict the actions of its users.
Analyze trends and events to predict, every day, how many customers will come to your stores.
A platform running 24/7 capable of delivering results on demand through a fully automated system.
Send the data is your only job. The platform does the rest for you.
The data flow is completely secure, the data is destroyed once the models are generated.
Our data scientists work every day to offer you the best platform, capable of creating models of exceptional quality, regardless of the use case or the field of application.
Manage the end-to-end process: create new projects, observe the learning phase, use the models as you wish, forecast in total autonomy
RapidMiner makes data science teams more productive through an open source platform for data prep, machine learning, and model deployment.
IBM Watson Analytics offers smart data discovery-from the cloud
Watson Analytics offers cognitive, predictive, & visual analytics in an easy-to-use service you can use on your own to find answers in your data. Even extend your data for a complete view of your business. For example, included with paid editions, users can tap directly into a sample of the Twitter Firehose to bring social insights into business decisions.
Wherever you are in your business, with Watson Analytics, you can get better data, understand your business, tell a story and think ahead.
"Watson Analytics gave me decision-making insight to my data in minutes. I tested the analytics against another program to see if the results would differ. Upon investigation, Watson Analytics gave me more detailed analysis with easy to communicate results in a few minutes as compared to days of setting up tests to run."
-Mark C. Lack - Manager, Strategy Analytics & BI, Mueller Inc.
Features and Benefits at a Glance
• Data preparation, refinement, management and analysis are automated and available from the cloud so you can easily work with your data-and trust the results.
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SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market.
SAS is the first company to call when you need to solve complex business problems, achieve key objectives and more effectively manage your information assets. As the leader in business analytics software and services, we provide a technology platform and market-leading analytic applications to help you not only navigate today's challenges but capitalize on tomorrow's opportunities.
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. Its ease of use; flexibility and scalability make IBM SPSS accessible to users with all skill levels and outfits projects of all sizes and complexity to help you and your organization find new opportunities, improve efficiency and minimize risk.
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.
Innovative Cloud Machine Learning platform with FREE, SaaS and on-premises versions.
Algorithmia makes applications smarter, by building a community around algorithm development, where state of the art algorithms are always live and accessible to anyone.