15 Most Popular Machine Learning Software Tools

We refer to any specialized software in artificial intelligence, self-iterated analysis of data, supervised learning, and other Machine Learning algorithms as machine learning software. Machine learning is applicable in a variety of software applications today, including email classification and human-computer interaction.

There is also machine learning specialized software for modeling, recruiting, design, and accountancy. Some ML toolkits, such as the ones discussed in this article, are customizable to your specific large datasets and process requirements.

Knowing which software package to utilize can be the distinction in designing a worthless bot and a fully functional AI system.

Key features of Machine Learning software

Pattern recognition, classification, and regression are all examples of pattern recognition techniques.
Predictive analytics for text and image retrieval
Functionality for reducing dimension
Vector machines that provide assistance
Collaboration with well-known machine learning libraries, such as Apache Spark MLlib
Works with popular programming languages such as Scala, Java, C++, R, Python, and others.
Full-stack open-source machine learning

1. Amazon ML

Amazon Machine Learning (AML) is a cloud-based and comprehensive machine learning software tool that can be utilized by online or mobile app developers of all skill levels. This managed service develops machine learning models and forecasting. Furthermore, it integrates data from multiple sources, including Redshift, Amazon S3, and RDS.

Wizards and visualization tools are provided by Amazon Machine Learning.
Supports three different types of models: multi-class classification, binary classification, and regression.
Allows users to construct a data source object from a MySQL database.
Furthermore, it allows users to create a data source object from data saved in Amazon Redshift.

2. Google ML Kit for Mobile

For mobile app developers, Google’s Android Team has created an ML KIT that combines machine learning and technical knowledge to produce more resilient and optimized applications to operate on a smartphone. This machine-learning software package helps in tasks such as face detection, text recognition, landmark detection, picture labeling, and barcode scanners. Its advantages include the availability of powerful technologies.

Depending on the needs, it can run on-device or in the cloud. Makes use of off-the-shelf software development solutions or bespoke models. The kit incorporates Google’s Firebase mobile development platform.

3. Apache Spark MLlib

It is a scalable machine learning library that runs on Apache Mesos, Hadoop, and Kubernetes, either alone or online. Furthermore, it may retrieve data from numerous data sources. To mention a few techniques for classification: naive Bayes, logistic regression, regression: general linear regression, and clustering: K-means. Its workflow tools include ML Pipeline creation, Feature transformations, ML persistence, and so forth.

Features include the ability to access Hadoop data sources such as HDFS, HBase, or local files. As a result, it is simple to integrate into Hadoop operations.It is compatible with Java, Scala, Python, and R. MLlib integrates with Spark’s APIs and works with NumPy in Python and R libraries. It includes high-quality algorithms and beats MapReduce.

4. Apple CoreML

Apple’s Core ML is a machine learning-based platform that assists you in integrating machine learning models into your mobile app. Drop the machine learning model file into your project, and Xcode will immediately generate a Swift wrapper class or Objective-C code. This approach is simple to use and can take use of all GPUs and CPUs for optimal performance.
It serves as the foundation for domain-specific frameworks and functionality.

Core ML supports Computer Vision for accurate image analysis, GameplayKit for assessing learnt decision trees, and Natural Language for natural language processing with ease. It has been meticulously tuned for on-device performance.

5. Apache Singa

The DB System Group at the National University of Singapore started developing this program in 2014, in partnership with the databases group at Zhejiang University. This artificial intelligence system helps in picture identification and natural language processing. It also supports a large range of well-known deep learning models. It has three key parts: IO, Core, and Model.

For more complex machine learning models, tensor abstraction is permitted. This application offers improved IO classes for writing, reading, encoding, and decoding files and data. It applies to asynchronous, synchronous, or hybrid training systems.

6. Apache Mahout

Apache Mahout is a Scala DSL and distributed linear algebra framework that is mathematically expressive. The Apache Software Foundation’s open-source and free project. This framework’s main purpose is to quickly develop an algorithm for mathematicians, data scientists, and statisticians. It offers machine learning techniques such as recommendation, clustering, and classification and a framework for creating scalable algorithms that are expandable.
It comes with matrix and vector libraries and uses the MapReduce paradigm to run on top of Apache Hadoop.

7. Shogun

It is an open-source, free machine learning library that was created in 1999 by Gunnar Raetsch and Soeren Sonnenburg. This software is implemented in C++. It actually provides methods and data structures to solve machine learning challenges. It also supports a wide range of programming languages, including R, Python, Java, Octave, C#, Ruby, Lua, and others.

Shogun focuses mostly on kernel machines, such as regression issues, and support vector machines for classification. This program lets you connect to various machine learning libraries such as LibLinear, LibSVM, SVMLight, LibOCAS, and others.

8. Accord.NET

It is a .Net machine learning foundation integrated with C# image and audio processing APIs. This system has several libraries for a variety of purposes, such as pattern recognition, statistical data processing, and linear algebra. It contains the Accord. Statistics, Accord.Math, and Accord.MachineLearning classes.

Features of Accord.Net

More than 40 non-parametric and parametric statistical distribution estimations are included.
Produces high-quality computer audition, computer vision, signal processing, and statistics programmes.
There are over 35 hypothesis tests in all, including two-way and one-way ANOVA tests, non-parametric tests like the Kolmogorov-Smirnov test, and many more.
It has over 38 kernel functions.

9. Google Cloud ML Engine

If you have millions or billions of training data points or the algorithm is highly advanced and takes a long time to execute properly, you should employ Google Cloud ML Engine to come to your aid. It is a cloud-based platform where machine learning app developers and data scientists can build and execute high-quality models.

Machine learning model training, construction, deep learning, and predictive modeling are all available. Businesses frequently use this application for a variety of purposes, including recognising clouds in satellite images and responding to consumer emails more quickly. It trains a complicated model in a variety of ways.

10. TensorFlow

It’s an open source machine learning library that can help you build ML models. Tensorflow was created by the Google team. It provides a versatile set of tools, libraries, and resources that enable researchers and developers to create and deploy machine learning systems. It aids in the development and training of your models. You may also use TensorFlow.js, a model converter, to execute your current models.

This is open source software that is extremely adaptable and can also compute numerically utilizing data flow graphs. It runs not only on GPUs and CPUs, but also a variety of mobile computing devices.

11. IBM Machine Learning

The IBM Machine Learning services allow you to mix and combine technologies such as IBM Watson Studio, IBM Watson Machine Learning, IBM Watson OpenScale, and IBM Cloud Pak for Data. Users may use open source software to construct AI models, deploy Models with your applications, and evaluate AI models. IBM Machine Learning also offers a free light plan with a cap of 20 CUH and a maximum of two simultaneous decisions optimizing batch tasks per installation.

12. Oryx 2

It is based on Apache Kafka and Apache Spark and is a manifestation of the lambda architecture. It is commonly utilized for large-scale real-time algorithms. Orxy2 is a software development platform that includes end-to-end apps for filtering, packaging, regression, classifying, and clustering. Oryx 2.8.0 is the most recent edition of this utility.

Oryx 2 is a more advanced version of the original Oryx 1 project.
It has three cooperative layers that work alongside each other: the speed layer, the batch layer, and the serving layer.
A data transport layer is also included, which transports data across levels and takes external input.

13. Neural Designer

Neural Designer is a rising machine learning service that allows you to avoid coding programmes and creating block diagrams by using a set of drag-and-drop and point-and-click capabilities. They have a superior average GPU training performance of 417K+ sample rate than several rival systems.

Neural Designer is fully written in C++, which compromises certain usability benefits in exchange for faster performance rates. Excellent memory management for loading large amounts of data. CPU and GPU optimization allows for quick computations.

14. Anaconda

Anaconda is a robust framework for the MLOps life cycle that is utilised by American National Bank, AT&T, Toyota, and Goldman Sachs, among others. A Conda package manager, unrestricted corporate products and connectivity, a replicated or cloud repository, and an environment administrator are all basic components. Personal subscriptions are ideal for freelancing since they are free to use and include hundreds of open-source tools and frameworks, as well as 7500+ Conda packages.

15. Azure Machine Learning

Azure Machine Learning from Microsoft allows customers to rapidly and simply construct, train, and deploy machine learning models. QA managers will appreciate the ability to use automated machine learning to identify relevant methods and hyperparameters more rapidly. Many of its enhancing features, such as some app services, event processing, and automation for up to 500 minutes of task duration, are available for free in perpetuity. It comes with robust collection of freebie add-ons and long free trial period plus monetary credits

Summary

Some machine learning algorithms are pre-designed to specialize in one area, but in this article, we saw only machine learning software applications that allow users to construct their own machine learning models for any data they have. We went through some of the most popular machine learning tools and how to utilize them for various purposes. Of course, with the scope of machine learning ever expanding, there are countless other libraries that haven’t made the list.