Everyone has probably heard of machine learning, but how exactly does it work? Does it mean that an intelligent machine makes decisions on behalf of humans? You might want to replace the term "intelligent machine" with "efficient algorithm" and add that this algorithm works with data. In doing so, it delivers a view that captures the essence of the data. Simply put, machine learning focuses on building models that learn from existing data and then uses those models to make logical decisions without requiring human intervention.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. As global enterprises continue to become bullish on data-driven applications, the companies providing the backend for these apps are seeing a massive surge in demand and investor support. Case in point: California-based Weka, which has just nabbed $135 million in an oversubscribed series D round, doubling its valuation to $750 million. Founded in 2013, Weka provides a software-based distributed file system, WekaFS, to make data available on demand for artificial intelligence (AI), machine learning (ML) and other compute-intensive workloads. The NVMe-based, I/O-intensive, low-latency solution -- that can also extend to object storage -- accelerates these applications, enabling data scientists, researchers and engineers to get to answers faster.

Machine learning (ML) is one approach for businesses to improve how they use large data to better understand their consumers' behaviour, happiness and loyalty. ML can look for patterns and abnormalities that users wouldn't think to look for on their own. Some machine learning algorithms are pre-programmed to specialise in a certain task, but in this article, we'll focus on machine learning tools that allow users to create their own machine learning methods for any data they have. Now, let's get down to the top 10 machine learning tools of 2021. Shogun toolbox, often known as Shogun, is a machine learning tool library that is independent and accessible to use.

Correctly Classified Instances 498 83.1386 % Incorrectly Classified Instances 101 16.8614 % Kappa statistic 0.6118 Mean absolute error 0.2621 Root mean squared error 0.3518 Total Number of Instances 599

WIRE)--WekaIO (Weka), the fastest-growing data platform for artificial intelligence/machine learning (AI/ML), life sciences research, enterprise technical computing, and high-performance data analytics (HPDA), today announced that it has been named a finalist in The Business Intelligence Group's inaugural Artificial Intelligence Excellence Awards program. The Artificial Intelligence Excellence Awards recognizes those organizations and products that are truly unique and making an impact on the market. Weka's Limitless Data Platform is built on a shareable, scalable, and distributed file storage system (WekaFS). Designed to help future-ready clients' data centers and enable digital transformation, the breakthrough WekaFS solution was architected to leverage the performance benefits of flash, high-speed networking, and compute acceleration technologies (like GPUs) whether the data resides on-premises, in the public cloud, or as a hybrid model. WekaFS stands out as a leading solution because it gives clients the leverage to get more value out of their compute resources, providing a full enterprise-grade solution with advanced security and full cloud integration.

Wikipedia: "Waikato Environment for Knowledge Analysis (Weka), developed at the University of Waikato, New Zealand, is free software licensed under the GNU General Public License, and the companion software to the book "Data Mining: Practical Machine Learning Tools and Techniques". Weka contains a collection of visualization tools and algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to these functions. The original non-Java version of Weka was a Tcl/Tk front-end to (mostly third-party) modeling algorithms implemented in other programming languages, plus data preprocessing utilities in C, and a Makefile-based system for running machine learning experiments. This original version was primarily designed as a tool for analyzing data from agricultural domains, but the more recent fully Java-based version (Weka 3), for which development started in 1997, is now used in many different application areas, in particular for educational purposes and research" As the title of the article suggests, WEKA is a tool that will allow you to do Machine Learning without any programming language but using only the GUI of the tool. In this article, we are going to show you how to launch WEKA, and how to start using it, what each of the components means, and help you decide if it is the right tool for your needs.

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.

No-code environments in machine learning have become increasingly popular due to the fact that almost anybody who needs machine learning, whatever field they may be in, can use these tools to build models for themselves. WEKA is one of the early no-code tools that was developed but is very efficient and powerful. WEKA can be used to implement state of the art machine learning and deep learning models and can support numerous file formats. In this article, we will learn about how to use WEKA to pre-process and build a machine learning model with code. WEKA can be used in Linux, Windows or Mac operating systems and you can download this from the official website here.

This article is part of a Machine Learning series. Our fourth expert is Dr. Eibe Frank, Associate Professor (Computer Science) at the University of Waikato, New Zealand. In this article, he talks about Weka and reveals what's under its hood. The idea behind Weka was to provide a uniform interface to a collection of machine learning algorithms in Java. This includes a graphical user interface, a command-line interface, and an API.

WekaIO has devised a "production-ready" framework to help artificial intelligence installations to speed up their storage data transfers. The basic deal is that WekaIO supports Nvidia's GPUDirect storage with its NVMe file storage. Weka says its solution can deliver 73 GB/sec of bandwidth to a single GPU client. The Weka AI framework omprises customisable reference architectures and software development kits, centred on Nvidia GPUs, Mellanox networking, Supermicro servers (other server and storage hardware vendors are also supported) and Weka Matrix parallel file system software. Paresh Kharya, director of product management for accelerated computing at Nvidia, provided a quote: "End-to-end application performance for AI requires feeding high-performance Nvidia GPUs with a high-throughput data pipeline. Weka AI leverages GPUDirect storage to provide a direct path between storage and GPUs, eliminating I/O bottlenecks for data intensive AI applications."