Machine learning has made a significant mark in 2020, with more people adopting it for better solutions.
Every passing year brings the digital world a whole new crop of buzzwords, phrases and technologies. Machine learning has made a significant mark in 2020 with more people getting familiar with the technology and adapting it for better solutions. Machine learning is a form of artificial intelligence that automates data analysis, allowing computers to learn through experience and perform tasks without human invasion or explicit programming. Machine learning is an astonishing technology. Mastering machine learning tools will let people play with data, train models, discover new methods, and create own algorithms. At present, the adoption of machine learning has tremendously increased amongst businesses. The number of machine learning tools has also grown in the same way. Out of a pile of machine learning tools, Analytics Insight brings you a list of top tools that are widely used by experts.
Scikit-Learn is a Python module for machine learning build on top of SciPy and is distributed under the 3-clause BSD license. It is an open-source machine learning package. Scikit-Learn is a simple and efficient tool for predictive data analysis. It is accessible to everybody and reusable in various contexts. Scikit-Learn assists in regression, clustering, classification, dimensionality reduction, and pre-processing.
Tensorflow is an end-to-end source platform for machine learning. It has a comprehensive, flexible ecosystem for tools, libraries, and community resources that lets researchers push state-of-the-art machine learning. Developers using TensorFlow finds it easy to build and deploy machine learning-powered applications. It is a blender of machine learning as well as neural network models.
Knime is again an open-source machine learning tool that is based on GUI. Knime is the leading open solution for data-driven innovation, designed for discovering the potential hidden in data, mining for fresh insights, or predicting new futures. Organizations can take their collaboration, productivity, and performance to the next level with a robust range of commercial extensions to the open-source platform.
Weka is an open-source software that provides tools for data pre-processing, implementation of several machine learning algorithms, and visualization tools so that users can develop machine learning techniques and apply them to real-world data mining problems. Users can access Weka through a graphical user interface. Besides, Weka lets users access other machine learning tools as well.
Accord.net is a computational machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing, and statistics applications even for commercial use. Accord libraries in the tool are very much useful in testing as well as manipulating audio files.
Mahout is launched by Apache, an open-source platform based on Hadoop. Apache Mahout is a distributed linear algebra framework and mathematically expressive Scala DSL designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms.
Cloud AutoML enables developers with limited machine learning expertise to train high-quality models specific to their business needs. It helps users build and deploy custom models for image, video, text, and tabular data with a robust toolset in one complete and cohesive platform. Google Cloud AutoML is very popular among companies.
Watson is an IBM cloud service that uses data to put machine learning and deep learning models into production. This machine learning tool allows users to perform training and scoring, two fundamental machine learning operations. IBM Watson is best suited for building machine learning applications through API connections.
Rapid Miner is a data science platform that has an amazing interface. It is platform-independent as it works on cross-platform operating systems. With the help of this tool, one can use their own data as well as test their own models. Its interface is very user-friendly and doesn’t need a programmer’s point of view.
OpenNN is an open-source neural networks library for machine learning. It solves many real-world applications in energy, marketing, health, etc. OpenNN contains sophisticated algorithms and utilities to deal with regression, classification, and forecasting. The platform offers users the perk of downloading its entire library for free from GitHub or SourceForge.