Best Machine Learning Software and Tools To Learn in 2019

Data scientists need an efficient and also effective machine learning software, tools or framework. For developing the system with the required training data to erase the drawbacks and make the machine or device intelligent. Just a well-characterized software can develop a productive machine. However, nowadays we develop our machine such that, we don’t need to give any instructions about the surroundings. The machine can act by itself, and also it can understand the environment. So we don’t need to guide it. Let us see the top 10 best machine learning software and tools.

Top 10 best machine learning software and tools-

1. Apache Mahout- 

Apache Mahout is a mathematically expressive Scala DSL and a distributed linear algebra framework. It is a free and open source venture of the Apache Software Foundation. The aim of this framework is to implement an algorithm rapidly for data scientists, mathematicians and also statisticians.

Features-

  • This framework used to build scalable algorithms.
  • Implementing machine learning techniques such as clustering, recommendation, and classification, collaborative filtering.
  • It includes matrix and vector libraries.
  • Run on top of Apache Hadoop using the MapReduce paradigm.

2. Shogun-

It is an open source machine learning library. This tool is written in C++. Literally, it provides data structures and also algorithms for machine learning problems. It supports many languages like Python, R, Octave, Java, C#, Ruby, Lua, etc. Shogun is easy combination of multiple data representations, algorithm classes and general purpose tools for rapid prototyping of data pipelines.

Features-

  • For large scale learning, this tool can be used.
  • Mainly, it focuses on kernel machines like support vector machines for classification and regression problems.
  • Allows linking to other machine learning libraries like LibSVM, LibLinear, SVMLight, LibOCAS, etc.
  • It can process a vast amount of data like 10 million samples.
  • It provides interfaces for Python, Lua, Octave, Java, C#, Ruby, MatLab, and R.

3. Amazon Machine learning-

It is a robust and cloud-based machine learning software which can be used by all skill levels of developers. For building machine learning models and generating predictions, this managed service can be used. It integrates data from multiple sources: Amazon S3, Redshift or RDS.

Features-

  • Amazon Machine Learning provides visualization tools and wizards.
  • AML supports binary classification, multi-class classification, and regression.
  • It also allows users to create a data source object from the MySQL database.
  • It permits users to create a data source object from data stored in Amazon Redshift.

4. Google cloud ML engine-

Cloud Machine Learning Engine is a managed service that allows developers and data scientists to build and run superior machine learning models in production. Cloud ML Engine offers training and prediction services, which can be used together or individually. It has been used by enterprises to solve problems ranging from identifying clouds in satellite images, ensuring food safety, and responding four times faster to customer emails. 

Features-

  • It provides ML model building, training, predictive modeling, and deep learning.
  • Cloud ML Engine has deep integration with our managed notebook service and our data services for machine learning.
  • Training and Online Prediction support multiple frameworks to train and serve classification, regression, clustering, and dimensionality reduction models.
  • The two services namely training and prediction can be used jointly or independently.
  • This software is used by enterprises, i.e., detecting clouds in a satellite image, responding faster to customer emails.
  • It can be used to train a complex model.

5. Accord.Net-

It is a .Net machine learning framework combined with audio and image processing libraries written in C#. This framework consists of multiple libraries for large number of applications, i.e., statistical data processing, pattern recognition, and linear algebra. It includes the Accord.Math, Accord.Statistics, and Accord.MachineLearning.

Features-

  • This framework is used for developing production-grade computer vision, computer audition, signal processing, and statistics applications.
  • It includes more than 40 parametric and non-parametric estimation of statistical distributions.
  • Also contains more than 35 hypothesis tests including one way and two-way ANOVA tests, non-parametric tests like Kolmogorov-Smirnov test and many more.
  • It has more than 38 kernel functions.

6. Apache Spark MLlib-

It is a machine learning library. Apache Spark MLlib runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access data from multiple data sources. It has several algorithms are like – logistic regression, naive Bayes, generalized linear regression, K-means, and many more. Its workflow utilities are Feature transformations, ML Pipeline construction, ML persistence, etc.

Features

  • It is easy to use. 
  • Apache Spark MLlib can be usable in Java, Scala, Python, and R.
  • MLlib fits into Spark’s APIs and inter-operates with NumPy in Python and R libraries.
  • Hadoop data source like HDFS, HBase, or local files can be used. So it is easy to plug into Hadoop workflows.
  • It contains high-quality algorithms and outperforms better than MapReduce.

7. Apple’s Core ML-

Apple’s Core ML is a machine learning framework which helps to integrate machine learning models into your app. You have to drop the ml model file into your project, and the Xcode create an Objective-C or Swift wrapper class automatically. Using the model is straightforward. It can leverage each CPUs and GPUs for maximum performance.

Features:

  • This library acts as a foundation for domain-specific frameworks and functionality.
  • Core ML supports Computer Vision for image analysis, Natural Language for natural language processing, and GameplayKit for evaluating learned decision trees.
  • It builds on top of low-level primitives.

8. TensorFlow-

TensorFlow is most popular and lovable by machine learning users. It’s an open source machine learning library which helps you to develop your ML models. Google team developed TensorFlow. It has a flexible tools, libraries, and resources that allows researchers and developers to build and deploy machine learning applications.

Features:

  • An end-to-end deep learning system.
  • Build and train ML models effortlessly using intuitive high-level APIs like Keras with eager execution.
  • Highly flexible.
  • Performs numerical computations using data flow graphs.
  • Run on CPUs or GPUs
  • Run on mobile computing platforms.
  • Efficiently train and deploy the model in the cloud.

9. Azure Machine Learning Studio-

Microsoft Azure Machine Learning Studio is a collaborative, drag-and-drop tool used to build, test, and deploy predictive analytics solutions on your data. This tool publishes models as web services that may be consumed by custom apps or BI tools.

Features-

  • This tool provides an interactive, visual workspace to build, test quickly, and iterate a predictive analysis model.
  • Doesn’t need programming. You have to just connect the datasets and modules visually to construct your predictive analysis model.
  • The connection of drag-and-drop datasets and modules form an experiment which you have to run in Machine Learning Studio.

10. Eclipse Deeplearning4j-

It is an open-source deep-learning library for the Java Virtual Machine (JVM). It is written in Java. Also it is compatible with any JVM languages like Scala, Clojure or Kotlin. The aim of Eclipse Deeplearning4j is to provide a prominent set of components for developing applications that integrate with Artificial Intelligence.

Features:

  • It allows configuring deep neural networks.
  • This covers the entire deep learning workflow from data preprocessing to distributed training, hyperparameter optimization and production-grade deployment.
  • It provides a flexible integration for large enterprise environments
  • Utilized at the edge to support the Internet of Things (IoT) deployments.

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