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Top Machine Learning Tools

by Kevin Roger
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When we talk about tools in machine learning, we must recognize that these are the frameworks where data analysts and scientists carryout different task algorithm the makes things easier for us all. Google, IMB and several others provide machine learning API using their cloud platforms where developers can easily design and build apps or toy with when coding.

The market for machine learning tools is large as many astounding interfaces and tools are being developed averagely in every six month to one year.

Amazon

This is a platform where you barely need a thorough knowledge of machine learning algorithm and skills. On Amazon Machine learning, analytics, model evaluation, and model training are the three operations offered for the building process of machine learning. Interestingly, Amazon Machine Learning allows an extensive and scalable smart application of machine learning algorithms. In addition, it has APIs real-time predictions for its applications.

OpenNN

On this platform are a large variety of materials like tutorial where learners can easily go about using the platform successfully. Although, OpenNN is a platform that has experienced audience target, it still has room for novice to grow and explore. With OpenNN tool like Neural Designer, you can simplify data entries by visualizations with graphs and charts. Simply, it is a programming cloud library for experienced developers in machine learning interested in executing neural networks in their designs.

Apache Mahout

Apache Mahout is from the mother software like popular Apache Hadoop. It is one of the Apache Software that enables developers to implement scalable algorithm for machine learning that looks into the area of classification, clustering, etc. it has a linear algebraic framework designed to enable statisticians to implement their algorithms. You should also know that most of the implementations made using this platform are dependent on the Apache Hadoop platform. What this mean? It simply implies that some level and extent of familiarity with Hadoop is crucial to be able to successfully and productively make use of Mahout.

Apache PredictionIO

This is another fun platform that creates templates for building machine learning engines. For PredictionIO, the inbuilt template system helps to reduce traditional lift in setting up a system and making specific predictions. PredictionIO, is an open source server for machine learning for developers. It is built on an open stack for conducting predictive engines for machine learning projects. Interestingly, it can be installed and used together with Spark, Elasticsearch etc., for simplifying and accelerating machine learning infrastructure.

Significantly, PredictionIo, can respond to queries when deployed as web service. While it does this, it can also unify an all-round data to create a more comprehensive predictive analytics.

Accord.NET

This is a platform that can be used on Xamarin, Microsoft Windows and more. The framework offers a unified API towards training and learning machine learning models. On Accord.NET, audio and image processing libraries are available having series of computational applications like recognition (pattern) and statistical data processing. Signal processing, computer vision and audition are highly possible algorithms that can be worked on Accord.NET because it is a framework for such.

Azure Machine Learning Workbench

It can support modeling in Spark, and Python. In 2017, Azure Machine Learning Model tool was released. And this is to aid developers in managing and deploying workflows and models while offering capabilities in the areas of capturing model telemetry for insights making, automated model training, model checking and versioning, etc.

TensorFlow

In 2015, Google TesnsorFlow was launched through Apache license. It is an advanced software library after DistBelief.  It could generate C++ and Python graph to be processed on either the GPUs or CPUs. Google TensorFlow is popular among programmers as it is efficient in facilitating simpler construction of complex and deep neural nets. It is also good for training and deployment of neural nets as well.

There several toolkits to ease learning, practicing and implementing machine learning. They are in-exhaustive. Many others include; Google APIs (e.g., Cloud Video Intelligence) for recognizing images and video objects thereby making them searchable, Microsoft Distributed Machine Learning Toolkit (DMLT) that allows you to build cluster of machine learning algorithms and run the applications at the same time. Also available is Microsoft Computational Network Toolkit (CNTK) capable of supporting videos images, and Recurrent Neural Network (RNN)

C++

This is a middle level language very effective for parallel computing on CUDA. It is considered as a middle level language because it has both the high and low level language feature. This language is popular among developers for its reliability, performance and application domains that it supports. An interesting part of C++is that it can be used for teaching and research as it has a good conceptual explanation that can be easily grasped. Its drivers and software development can interact directly with hardware even in realtime constraints.

Rapid Miner

When dealing the three folds such as machine learning, deep learning, text mining, predictive analytics, in application development, Rapid Miner does great.  Analytical workflow can be designed and implemented using its graphical user interface (GUI). It also has standard model visualization and optimization.

TensorFlow

In 2015, Google TesnsorFlow was launched through Apache license. It is an advanced software library after DistBelief.  It could generate C++ and Python graph to be processed on either the GPUs or CPUs. Google TensorFlow is popular among programmers as it is efficient in facilitating simpler construction of complex and deep neural nets. It is also good for training and deployment of neural nets as well. And, a problem with TensorFlow is that it is difficult to learn.

PyTorch

From the name, it is a Python machine learning library. It has a Lua-based computation framework. PyTorch is efficient in building neural networks via Autograd Module, and it has various optimization algorithms for building neural networks. When dealing with graphs, it is good for computational graphs and it is easy to use because of its hybrid frontend.

Weka

When looking for machine learning algorithm for data mining, Weka is good for associated rules mining, data classification and regression. Plus, it is easy to learn.

KNIME

This is similar in a way with Weka as it is effective for data mining as well. It is a tool for data integration platform, reporting and data analytics. KNIME can be sufficiently used for Business intelligence, financial, CRM and can work as SAS alternative. A limitation to this tool is that, its visualization and exporting ability is limited.

Colab

You can use Colab directly from your Google drive. For machine learning education, to assisting machine leering research, Colab is useful. In addition, Google Colab supports Python and can build machine learning apps via using PyTorch libraries, TensorFlow and few others.

Keras

It is written in Python and is productive when used in quick research. Mainly, Keras is a neural network API that can run on the combination of two networks (CPU and GPU). Kera framework is extensive and user-friendly. In addition, it is easy and fast when prototyping.

There several toolkits to ease learning, practicing and implementing machine learning. They are in-exhaustive. Many others include; Google APIs (e.g., Cloud Video Intelligence) for recognizing images and video objects thereby making them searchable, Microsoft Distributed Machine Learning Toolkit (DMLT) that allows you to build cluster of machine learning algorithms and run the applications at the same time. Also available is Microsoft Computational Network Toolkit (CNTK) capable of supporting videos images, and Recurrent Neural Network (RNN).

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