Machine learning is a sub-component of Artificial Intelligence. It can be defined as the algorithms that parse data sets and then learn from it to apply what has been learned to make informed decisions. In the case of Machine learning, the computer program learns from experience by performing some tasks and sees how those tasks’ performance improves with the experience.

It is the state of the art field of AI that is used extensively in developing tools for industry and society. The machine learning algorithms focus on solving real-world issues by automated tasks across industries. These may range from on-demand music services to data security services.

The application of AI is increasing day by day as it is making life easy. The global market of AI is expected to be almost $60 billion. As AI enhances business productivity not only big shots but also startups are investing in AI. Since the adoption of AI and machine learning has tremendously increased, the number of software tools used for the purpose of machine learning has increased at the same rate. In this blog, we have discussed 10 most popular machine learning tools presently dominating the market.

1.TensorFlow

TensorFlow is an open-source machine learning library that helps you to develop your ML models. Developed by Google TensorFlow offers a JS library that helps in machine learning development. With its flexible schemes of tools, resources, and libraries you can build and deploy machine learning applications.

Features:

• Helps to build and train your ML models.
• TensorFlow.js works as a model converter and can run existing models.
• It helps in neural networks.
• It promotes a cycle deep learning system.
• With the use of high-level APIs like Keras, you can train as well as build ML models effortlessly.
• This is open-source software and highly flexible.
• Using a data flow graph can also perform numerical computations.
• It runs on GPUs and CPUs, and also on various mobile computing platforms.
• Efficiently deploy and train the model in the cloud.

Tool Cost/Plan Details: Free

Official Website: Tensorflow

2.Google Cloud ML Engine

Google Cloud ML Engine is a hosted platform on Google where developers and data scientists can run and create optimum quality machine learning models. This software is useful when you need millions or billions of data to train your classifier. This is specifically useful when the algorithm is quite sophisticated.

Features:

• Helps to create machine learning model training, building, deep learning, and predictive modeling.
• You can use the two services – prediction and training either independently or jointly.
• Enterprises widely use this software. Some of its usages is detecting clouds in a satellite image, Responding faster to emails of customers.
• It can be widely used to train a complex model.

Tool cost/Plan details – Google Cloud ML is not free. You will get $300 credit for the first sign-up. They just charge you when you run it.

Official website: https://cloud.google.com/sdk/gcloud/reference/ml-engine

3.PyTorch

PyTorch is a Python machine learning library. The torch is a Lua-based computing framework, scripting language, and machine learning library.

Related post – Why is Python not the programming language of the future?

Features:

• Using Autograd Module helps in building neural networks.
• You will have a variety of optimization algorithms for building neural networks.
• It provides distributed training, various tools, and libraries.
• PyTorch can be used on cloud platforms.

Tool Cost/Plan Details: Free

Official Website: Pytorch

4.Apache Mahout

Apache Mahout follows distributed linear algebra framework and is a mathematically expressive Scala DSL framework. It is an open-source free project from Apache Software Foundation. It helps to implement an algorithm promptly for mathematicians, statisticians, and data scientists.

Features:

• Implements machine learning techniques including recommendation, classification and clustering.
• It is an extensible framework that helps to build scalable algorithms.
• It includes matrix and vector libraries.
• Run-on top of Apache Hadoop using the MapReduce paradigm.
• It provides algorithms for Pre-processors, Regression, Clustering, Recommenders, and Distributed Linear Algebra

Tool Cost/Plan Details: Free

Official Website: Mahout – Apache

5.Accord.Net

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

Features:

• It has more than 38 kernel functions
• Consists of more than 40 non-parametric and parametric estimation of statistical distributions.
• Contains more than 35 hypothesis tests that include two-way and one-way ANOVA tests, non-parametric tests such as the Kolmogorov-Smirnov test, and many more.
• Used for creating production-grade computer audition, computer vision, signal processing, and statistics apps.

Tool Cost/Plan Details: Free

Official Website: Accord.net

6.Rapid Miner

Rapid Miner provides a platform for machine learning, deep learning, text mining, data preparation, and predictive analytics. It can be used for research, education, and application development.

Features:

• Through its GUI, you can design and implement analytical workflows.
• It helps with data preparation.
• Model validation and optimization.
• Result Visualization.

Tool Cost/Plan Details:

It has four plans:

• Free plan
• Small: $2500 per year.
• Medium: $5000 per year.
• Large: $10000 per year.

Official Website: Rapid Miner

7.Shogun

It is an open-source and free machine learning library written in the C++ language. It offers algorithms and data structures for machine learning problems. Besides, it supports many languages like R, Python, Java, Octave, C#, Ruby, Lua, etc.

Features:

• It mainly focuses on kernel machines that emphasize regression problems and support vector machines for classification.
• You can use this tool for large-scale learning.
• This tool allows linking to other machine learning libraries like LibLinear, LibSVM, SVMLight, LibOCAS, etc.
• It also provides interfaces for Lua, Python, Java, C#, Octave, Ruby, MatLab, and R.
• It can process a large amount of data such as 10 million samples.
• It helps in implementing Hidden Markov models.

Tool Cost/Plan Details: Free

Official Website: Shogun

8.Apache Spark MLib

It is a scalable machine learning library and runs on Apache Hadoop, Mesos, Kubernetes, standalone, or in the cloud. Additionally, it can access data from multiple data sources. For classification it uses a wide array of algorithms like: Regression: generalized linear regression, naive Bayes, logistic regression, Clustering: K-means, to name a few. Its workflow utilities are ML Pipeline construction, ML persistence, Feature transformations,  etc.

Features:

• Hadoop data source like HDFS, HBase, or local files can be used. So it is easy to plug into Hadoop workflows.
• Ease of use. It 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.
• It contains high-quality algorithms and outperforms better than MapReduce.

Tool Cost/Plan Details: Free

Official Website: https://spark.apache.org/mllib/

9.Knime

KNIME is used as a data analysis, reporting and integration platform. It uses the data pipelining concept, and combines different components for machine learning and data mining.

Features:

• It can integrate the code of programming languages like C, C++, R, Python, Java, JavaScript etc.
• It can be used for business intelligence, financial data analysis, and CRM.

Tool Cost/Plan Details: Free

Official website: KNIME

10.Scikit-learn

Scikit-learn is for machine learning development in python. It provides a library for the Python programming language.

Features:

• It helps in data mining and data analysis.
• It provides models and algorithms for Classification, Regression, Clustering, Dimensional reduction, Model selection, and Pre-processing.

Tool Cost/Plan Details: Free.

Official Website: scikit-learn

Final verdict:

Hope the machine learning tools mentioned above can ease your software development problems in an effective way. It is expected that with the help of these tools, your customers can get an effective software development solutions as per their requirements.