In this article, we will present some of the best machine learning tools to look out for in 2022
Apache Mahout: It is an open-source project to create scalable, machine learning algorithms. It is a distributed linear algebra framework and mathematically expressive Scala DSL designed to let mathematicians and statisticians.
Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. ML software tools are algorithms applications of AI that give systems the ability to learn and improve without ample human input. They allow the software to become more accurate in predicting outcomes without being explicitly programmed. There are also ML specialty software for things like simulation, recruitment, architecture, and accounting. In this article, we will present some of the best machine learning tools and outline how users can best leverage each for deep learning, data mining, and dataset visualization.
Scikit-learn: It is a free software machine learning library for the Python programming language. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction via a consistent interface in Python.
TensorFlow: It is an end-to-end open-source machine learning platform. It is a rich system for managing all aspects of a machine learning system. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources.
KNIME: It is a softer approach to machine learning automation. It makes understanding data and designing data science workflows and reusable components accessible to everyone.
Colab: It is a convenient and easy-to-use way to run Jupyter notebooks on the cloud. And it is especially well suited to machine learning, data analysis, and education. It’s not only to solve the storage problems of working with a large dataset but also the financial constraints of affording a system that meets data science work requirements.
Shogun Machine Learning: It is a free, open-source machine learning software library written in C++ and offers numerous algorithms and data structures for ML problems. It ensures that the underlying algorithms are transparent and accessible.
Rapid Miner: It is an awesome visual workflow designer. It provides data mining and machine learning procedures including data loading and transformation (ETL), data preprocessing and visualization, predictive analytics, and statistical modeling, evaluation, and deployment.
Amazon Machine Learning: It is an Amazon Web Services product that allows a developer to discover patterns in end-user data through algorithms, construct mathematical models based on these patterns and then create and implement predictive applications.
Apache Spark MLlib: It is a scalable machine learning library, with APIs in Java, Scala, Python, and R. It provides tools such as ML Algorithms: common learning algorithms such as classification, regression, clustering, and collaborative filtering.
Core ML Machine Learning Algorithm: It applies an ML algorithm to a set of training data to create a model. It supports Vision for analyzing images, Natural Language for processing text, Speech for converting audio to text, and Sound Analysis for identifying sounds in audio.
Source: analyticsinsight.net
