Almost all organisations are adopting emerging technologies such as machine learning and data science. These machine learning frameworks are meant for the developers who work using Java language. In this article, we list you 8 machine learning frameworks for Java developers.

1| Apache SAMOA

Apache Scalable Advanced Massive Online Analysis (SAMOA) is a distributed streaming machine learning framework which contains a programming abstraction for distributed streaming machine learning algorithms. It provides a collection of distributed streaming algorithms for the most common data mining and machine learning tasks such as classification, clustering, and regression. Apache SAMOA enables the development of new machine learning algorithms without dealing with the complexity of underlying streaming processing engines as well as provides extensibility in integrating new SPEs into the framework.

2| AMIDST ToolBox

AMIDST is an open source Java toolbox for scalable probabilistic machine learning with a special focus on streaming data. It allows specifying probabilistic graphical models with latent variables and temporal dependencies. AMIDST provides tailored parallel and distributed implementations of Bayesian parameter learning for batch and streaming data. This processing is based on flexible and scalable message passing algorithms. The features of this toolbox include probabilistic graphical models, scalable inference, data streams, large-scale data, extensible and interoperability.

3| Apache Mahout

Apache Mahout is a distributed linear algebra framework and mathematically expressive Scala DSL which is designed to quickly implement the machine learning algorithms. This framework mainly focuses on clustering, classification, and filtering. Running any application which uses Mahout will require installing a binary or source version and setting the environment.

4| Datumbox

The Datumbox machine learning framework is an open-source framework written in Java which allows the rapid development of machine learning and statistical applications. The main focus of the framework is to include a large number of machine learning algorithms & statistical methods and to be able to handle large-sized datasets.

The framework currently supports performing multiple parametric & non-parametric statistical tests, calculating descriptive statistics on censored & uncensored data, performing ANOVA, cluster analysis, dimension reduction, regression analysis, time series analysis, sampling and calculation of probabilities from the most common discrete and continues Distributions. In addition, it provides several implemented algorithms including Max Entropy, Naive Bayes, SVM, Bootstrap Aggregating, Adaboost, Kmeans, Hierarchical Clustering, Dirichlet Process Mixture Models, Softmax Regression, Ordinal Regression, Linear Regression, Stepwise Regression, PCA, etc.

5| ELKI

ELKI is an open source data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection. It aims at providing a large collection of highly parameterizable algorithms, in order to allow easy and fair evaluation and benchmarking of algorithms. In ELKI, data mining algorithms and data management tasks are separated and allow for an independent evaluation. This separation makes ELKI unique among data mining frameworks like Weka or Rapidminer and frameworks for index structures like GiST.

6| Encog

Encog is a pure Java/C# machine learning framework which is created in 2008 to support genetic programming, NEAT/HyperNEAT, and other neural network technologies. This framework supports a variety of advanced algorithms, as well as support classes to normalize and process data. Machine learning algorithms such as Support Vector Machines, Neural Networks, Bayesian Networks, Hidden Markov Models, Genetic Programming and Genetic Algorithms are supported. Most Encog training algorithms are multi-threaded and scale well to multicore hardware.

7| Neuroph

Neuroph is an open source, lightweight Java neural network framework to develop common neural network architectures. It contains well designed, open source Java library with a small number of basic classes which correspond to basic NN concepts. This framework also has a nice GUI neural network editor to quickly create Java neural network components.

8| Smile

Smile (Statistical Machine Intelligence and Learning Engine) is a fast and comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala. It covers every aspect of machine learning with neat interfaces, including classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, efficient nearest neighbour search, etc.

The post 8 Machine Learning Frameworks Java Developers Must Try In 2019 appeared first on Artificial Intelligence.

1| Apache SAMOA

Apache Scalable Advanced Massive Online Analysis (SAMOA) is a distributed streaming machine learning framework which contains a programming abstraction for distributed streaming machine learning algorithms. It provides a collection of distributed streaming algorithms for the most common data mining and machine learning tasks such as classification, clustering, and regression. Apache SAMOA enables the development of new machine learning algorithms without dealing with the complexity of underlying streaming processing engines as well as provides extensibility in integrating new SPEs into the framework.

2| AMIDST ToolBox

AMIDST is an open source Java toolbox for scalable probabilistic machine learning with a special focus on streaming data. It allows specifying probabilistic graphical models with latent variables and temporal dependencies. AMIDST provides tailored parallel and distributed implementations of Bayesian parameter learning for batch and streaming data. This processing is based on flexible and scalable message passing algorithms. The features of this toolbox include probabilistic graphical models, scalable inference, data streams, large-scale data, extensible and interoperability.

3| Apache Mahout

Apache Mahout is a distributed linear algebra framework and mathematically expressive Scala DSL which is designed to quickly implement the machine learning algorithms. This framework mainly focuses on clustering, classification, and filtering. Running any application which uses Mahout will require installing a binary or source version and setting the environment.

4| Datumbox

The Datumbox machine learning framework is an open-source framework written in Java which allows the rapid development of machine learning and statistical applications. The main focus of the framework is to include a large number of machine learning algorithms & statistical methods and to be able to handle large-sized datasets.

The framework currently supports performing multiple parametric & non-parametric statistical tests, calculating descriptive statistics on censored & uncensored data, performing ANOVA, cluster analysis, dimension reduction, regression analysis, time series analysis, sampling and calculation of probabilities from the most common discrete and continues Distributions. In addition, it provides several implemented algorithms including Max Entropy, Naive Bayes, SVM, Bootstrap Aggregating, Adaboost, Kmeans, Hierarchical Clustering, Dirichlet Process Mixture Models, Softmax Regression, Ordinal Regression, Linear Regression, Stepwise Regression, PCA, etc.

5| ELKI

ELKI is an open source data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection. It aims at providing a large collection of highly parameterizable algorithms, in order to allow easy and fair evaluation and benchmarking of algorithms. In ELKI, data mining algorithms and data management tasks are separated and allow for an independent evaluation. This separation makes ELKI unique among data mining frameworks like Weka or Rapidminer and frameworks for index structures like GiST.

6| Encog

Encog is a pure Java/C# machine learning framework which is created in 2008 to support genetic programming, NEAT/HyperNEAT, and other neural network technologies. This framework supports a variety of advanced algorithms, as well as support classes to normalize and process data. Machine learning algorithms such as Support Vector Machines, Neural Networks, Bayesian Networks, Hidden Markov Models, Genetic Programming and Genetic Algorithms are supported. Most Encog training algorithms are multi-threaded and scale well to multicore hardware.

7| Neuroph

Neuroph is an open source, lightweight Java neural network framework to develop common neural network architectures. It contains well designed, open source Java library with a small number of basic classes which correspond to basic NN concepts. This framework also has a nice GUI neural network editor to quickly create Java neural network components.

8| Smile

Smile (Statistical Machine Intelligence and Learning Engine) is a fast and comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala. It covers every aspect of machine learning with neat interfaces, including classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, efficient nearest neighbour search, etc.

The post 8 Machine Learning Frameworks Java Developers Must Try In 2019 appeared first on Artificial Intelligence.

Big data analysts need the right tools which empower them to analyse and make robust decisions in an organisation. In this article, Analytics India Magazine lists down 15 top analytical tools that all persons who work with Big Data must use in 2019:

1| Apache Spark

Apache Spark is a fast and general-purpose cluster computing system which provides high-level APIs in Java, Scala, Python, and R, and an optimised engine which supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming. Some of the features of this unified analytics engine include

• Speed: This tool achieves high performance for both batch and streaming data.
• Easy to use: It offers over 80 high-level operators which makes it easy to build parallel applications
• Generality: Includes a stack of libraries which can be combined seamlessly in the same application
• Flexible to work on almost everywhere. It runs on Hadoop, Apache Mesos, Kubernetes, etc.

2| Apache Storm

Apache Storm is a free and open source distributed real-time computation system which makes it easy to reliably process unbounded streams of data, doing for real-time processing like Hadoop for batch processing. The features of this analytics tool include

• Simple: Storm is simple, can be used with any programming language
• Fast: A benchmark clocked it at over a million tuples processed per second per node
• Scalable: It is scalable, fault-tolerant and guarantees your data will be processed.
• Easy to use: This tool is easy to set up and operate.

3| Apache SAMOA

Apache SAMOA is a platform for mining big data streams. It provides a collection of distributed streaming algorithms for the most common data mining and machine learning tasks such as classification, clustering, and regression, as well as programming abstractions to develop new algorithms that run on top of distributed stream processing engines (DSPEs).

The features of this analytics tool include

• SAMOA’s main goal is to help developers to create easily machine learning algorithms on top of any distributed stream processing engine.
• The users can develop distributed streaming ML algorithms once and execute them on multiple DSPEs.

4| Apache Hadoop

The Apache Hadoop software library is a framework which allows for the distributed processing of large data sets across clusters of computers using simple programming models. The framework is composed of the following modules

• Hadoop Common: The common utilities that support the other Hadoop modules.
• Hadoop Distributed File System (HDFS): A distributed file system that provides high-throughput access to application data.
• Hadoop YARN: A framework for job scheduling and cluster resource management.
• Hadoop MapReduce: A YARN-based system for parallel processing of large data sets.
• Hadoop Ozone: An object store for Hadoop.
• Hadoop Submarine: A machine learning engine for Hadoop.

5| Apache Cassandra

Apache Cassandra is a distributed database which is highly scalable without any compromising performance. It is a perfect platform for mission-critical data as it has features such as linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure.

Some of the features of this analytics tool include

• Decentralised: There are no single points of failure as every node in the cluster is identical.
• Performant: Cassandra consistentlyoutperformspopular NoSQL alternatives in benchmarks and real applications, primarily because of fundamental architectural choices.
• Fault Tolerant: Data is automatically replicated to multiple nodes for fault-tolerance.
• Durable: Cassandra is suitable for applications that can’t afford to lose data, even when an entire data centre goes down.

6| Elasticsearch

Elasticsearch is a highly scalable open-source full-text search and analytics engine which allows you to store, search, and analyse big volumes of data quickly and in near real time. It is a distributed, RESTful search and analytics engine capable of solving a growing number of use cases. Some of the features of this analytics tool include

• Query: Elasticsearch lets you perform and combine many types of searches — structured, unstructured, geo, metric — any way you want.
• Analyse: Elasticsearch aggregations let you zoom out to explore trends and patterns in your data.
• Speed: Elasticsearch if incredibly fast due to the implementation of inverted indices with finite state transducers for full-text querying, BKD trees for storing numeric and geodata, and a column store for analytics.
• Fast time-to-value: Elasticsearch offers simple REST-based APIs, a simple HTTP interface, and uses schema-free JSON documents, making it easy to get started and quickly build applications for a variety of use-cases.

7| Knime

KNIME Analytics Platform is the leading open solution for data-driven innovation, helping you discover the potential hidden in your data, mine for fresh insights, or predict new futures. It is an enterprise-grade, open source platform which is fast to deploy, easy to scale, and intuitive to learn. KNIME Analytics Platform is easy to use and it is one of the perfect tools for a data scientist.

8| Lumify

LUMIFY is powerful big data fusion, analysis, and visualisation platform which supports the development of actionable intelligence. The features of Lumify include

• Speed and Scale: Queries run as fast as your underlying database can support, allowing you to take advantage of your existing data infrastructure for data ingest, streaming, complex queries, etc.
• Non-Proprietary Data Storage: Lumify sits on top of standard data platforms and fits into your analytic eco-system. Lumify works with your existing data to enable sharing across your analytic tools and systems.
• Bring Your Own Analytics Capability: Lumify’s infrastructure allows you to attach new analytic tools that will work in the background to monitor changes and assist analysts as they sort through complex information.
• Real-Time and Secure Collaboration: Analysts can instantly share their workspaces with their colleagues, control individual access, and set separate controls based on security classification.

9| MongoDB

MongoDB is a document database with the scalability and flexibility which is designed for ease of development and scaling. It is open sourced and offers both a Community and an Enterprise version of the database. Some of the features include

• MongoDB stores data in flexible, JSON-like documents, meaning fields can vary from document to document and data structure can be changed over time.
• The document model maps to the objects in your application code, making data easy to work with.
• Ad hoc queries, indexing, and real-time aggregation provide powerful ways to access and analyse your data.
• MongoDB is a distributed database at its core, so high availability, horizontal scaling, and geographic distribution are built in and easy to use.

10| Neo4j

Neo4j is one of the popular graph database management systems. Neo4j’s Graph Platform is the fastest path available to operationalise enterprise analytic insights by connecting the work of big data IT to data scientists to application developers building impactful applications. The Graph Platform fits seamlessly into enterprise data architectures, alongside, around and above relational warehouses, data lakes, cloud and legacy systems.

11| NodeXL

NodeXL Basic is a free, open-source template for Microsoft Excel which makes it easy to explore network graphs. NodeXL Pro offers additional features that extend NodeXL Basic, providing easy access to social media network data streams, advanced network metrics, and text and sentiment analysis, and powerful report generation.

12| R

R is one of the most popular statistical languages for statistical computing and graphics. It provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, etc,) and graphical techniques, and is highly extensible.

13| RapidMiner

RapidMiner Studio is a powerful data mining tool for rapidly building predictive models. It features hundreds of data preparation and machine learning algorithms to support all your data mining projects. With RapidMiner Studio, you can access, load and analyse any type of data – both traditional structured data and unstructured data like text, images, and media. Some of the features include

• Easy to use visual environment for building analytics processes
• More than 1,500 operators for all tasks of data transformation and analysis
• Support for scripting environments like R, or Groovy for ultimate extensibility
• Seamlessly access and use of algorithms from H2O, Weka and other third-party libraries
• Extensible through open platform APIs and a Marketplace with additional functionality.

14| Tableau

Tableau is one of the most popular BI tools which is used for data visualisation. The tool allows data blending, real-time collaboration, etc. and are able to connect to the files and other Big Data sources in order to gain insights and patterns from data. It can be said as the most powerful, secure, and flexible end-to-end analytics platform for your data.

15| Talend

Talend is an open source data integration and data management platform, which has a number of ETL tools which are designed to simplify the complex needs of a growing, data-driven business. Talend Open Studio for Big Data helps in developing faster with a drag-and-drop UI and pre-built connectors and components.

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