Different Tools of Machine Learning and their Application

Machine learning is an excellent technology. It can also be incredibly overwhelming if used appropriately. What a fantastic prospect it would be to create a machine that behaved almost entirely like a human person. Learning how to use machine learning tools will allow you to explore with data, train your models, discover new approaches, and design your algorithms.

Machine learning comes with many ML tools, platforms, and applications. Furthermore, ML technology is always growing. To develop experience, you must select one of several machine learning technologies. This article lists the Different tools of Machine Learning and their application.

TensorFlow

TensorFlow provides a JS library that aids in the creation of machine learning algorithms. Its APIs will assist you in creating and training models. It is an open-source machine learning library that aids in developing ML models. The Google team created it. It provides a versatile set of tools, libraries, and resources that enable researchers and developers to create and deploy machine learning systems.

Features:

• It assists in the development and training of your models.
• You may also use TensorFlow.js, a model converter, to execute your current models.
• It supports the neuronal network.
• A deep learning system that goes through the whole learning cycle.
• Train and create ML models with ease utilizing high-level APIs.
• This is open-source software that is extremely adaptable.
• It can also compute numerically utilizing data flow graphs.
• It runs on GPUs and CPUs and a variety of mobile computing systems.
• Deploy and train the model on the cloud in an efficient manner.
• Details about the tool’s cost/plan: None.

Google Cloud ML Engine

If you are developing your classifier on a large amount of data, your Desktop pc may perform admirably. But what if you have millions or billions of training data points? Or is the algorithm highly advanced and takes a long time to perform 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 machine learning models.

Features:

• Machine learning model training, development, deep learning, and predictive modeling are available.
• The two services, prediction, and training can be utilized separately or in tandem.
• Businesses frequently use this program for various purposes, including recognizing clouds in satellite images and responding to consumer emails more quickly.
• It is used to train a complicated model in a variety of ways.

Amazon Machine Learning (AML)

Amazon Machine Learning is a cloud-based and comprehensive machine learning software tool that can utilize online or mobile app developers of all skill levels. This managed service is commonly used for developing machine learning models and forecasting.

Features:

• Amazon Machine Learning includes wizards and visualization tools.
• Provides three different types of models: multi-class classification, binary classification, and regression.
• Users can construct a data source object from the MySQL database.
• Furthermore, it allows users to create a data source object from data saved in Amazon Redshift.

Accord.NET

It is an a.Net machine learning paradigm integrated with C# image and audio processing APIs. This framework comprises several libraries that may be used for various applications, such as pattern recognition, statistical data processing, and linear algebra.

Features:

• Produces high-quality computer audition, computer vision, signal processing, and statistics programs.
• 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.

Apache Mahout

Apache Mahout is a Scala DSL and distributed linear algebra framework that is mathematically expressive. It is an Apache Software Foundation open-source and free project. This framework’s main purpose is to quickly develop an algorithm for mathematicians, data scientists, and statisticians.

Features

• Machine learning techniques such as recommendation, clustering, and classification are used.
• A framework for creating scalable algorithms that is expandable.
• It comes with matrix and vector libraries.
• Uses the MapReduce paradigm to run on top of Apache Hadoop.

Shogun

It is an open-source, free machine learning library created in 1999 by Gunnar Raetsch and Soeren Sonnenburg. This application was created using the C programming language. It provides techniques and data structures to help with machine learning challenges.

Features:

• It focuses mostly on kernel machines
• Initially, this technology was intended for large-scale learning.
• This tool allows you to connect to various machine learning libraries.
• It is capable of processing massive amounts of data.

Oryx 2

It is based on Apache Kafka and Apache Spark and manifests the lambda architecture. It is commonly used for large-scale real-time machine learning. It is a software development platform that includes end-to-end applications.

Features

• It contains three tiers: top-level specialization giving ML abstractions, generic lambda architecture tier, and end-to-end implementation of the same basic ML algorithms.
• The Oryx 2 project is an improved version of the original Oryx 1 project.
• It comprises three cooperating layers that work side by side: the speed layer, the batch layer, and the serving layer.
• A data transport layer also transports data between levels and takes input from external references.

Apache Spark MLlib

An efficient and scalable library operates on Hadoop, Apache Mesos, and Kubernetes, either standalone or in the cloud. Furthermore, it may retrieve data from numerous data sources. It is workflow tools include ML Pipeline creation, Feature transformations, ML persistence.

Features

• The ability to access Hadoop data sources.
• MLlib integrates with Spark’s APIs.
• It includes high-quality algorithms and beats MapReduce.

Google ML Kit for Mobile

Google’s Team has created an ML KIT that combines machine learning and technical skills to produce more robust, optimized, and tailored apps to operate on a device. This machine-learning software package may be used for face detection, text recognition, landmark detection, picture labeling, and barcode scanning.

Features:

• It offers cutting-edge technology.
• 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.

Conclusion

Machine Learning is a branch of Artificial Intelligence which enables the machine to learn from the past data or experiences without programming.  Check out the artificial intelligence course to explore more about the subject and kick start your career as an Artificial intelligence Engineer.

I hope these machine learning technologies may help you with your software development challenges. With the aid of these technologies, you will be able to deliver efficient software development solutions to your clients based on their needs. 

Great Learning provides top-rated online artificial intelligence courses that will guide you through the process of merging deep learning, machine learning, and data science approaches to construct your own AI and tackle unique issues in any business.