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Data Science


Global Big Data Conference

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Data science has reached its peak through automation. All the phases of a data science project -- like data cleaning, model development, model comparison, model validation, and deployment -- are fully automated and can be executed in minutes, which earlier would have taken months. Machine learning (ML) continuously works to tweak the model to improve predictions. It's extremely critical to set up the right data pipeline to have a continuous flow of new data for all your data science, artificial intelligence (AI), ML, and decision intelligence projects. Decision intelligence (DI) is the next major data-driven decision-making technique for disruptive innovation after data science. Futuristic – Models ML outcomes to predict social, environmental, and business impact.


Day 29 : 60 days of Data Science and Machine Learning Series

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ML clustering Project 2 ( Part 2)... “Day 29 : 60 days of Data Science and Machine Learning Series” is published by Naina Chaturvedi in Coders Mojo.


Why AI-enabled decision-making is the next step in the supply chain digitalisation journey

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As a result, companies have gone through a decade's worth of digital transformation in just a matter of months, with the pandemic forcing them to refresh archaic processes with AI, machine learning, and data science technologies. Such technological advancements will continue to evolve and further establish themselves as a critical component to managing complex logistical landscapes – from improving efficiency and mitigating the effects of a global labour shortage, to identifying more robust and dependable ways to move commodities. In a world where uncertainty is the only certainty, AI-enabled order and inventory visibility across shipments will also be vital to'keep the wheels in motion.' Most importantly, to provide real-time updates on changes to arrival times and to identify potential disruptions before and as they occur. Take the recent congestion issues at the Port of Los Angeles, for example.


25 Best edX Courses for Data Science and Machine Learning

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The course material of this course is available freely. But for the certificate, you have to pay. In this course, you will learn the foundational TensorFlow concepts such as the main functions, operations, and execution pipelines. This course will also teach how to use TensorFlow in curve fitting, regression, classification, and minimization of error functions. You will understand different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks, and Autoencoders.


4 Reasons why you need data integration tool DataScienceCentral.com

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We are in a time when information is the core element of business success for companies in almost any industry. As technologies emerge and find large-scale adoption, there is an influx of massive amounts of data within enterprises. Two primary challenges need to be solved to obtain the necessary information. First is trustable information you can take action on without questioning. That's a problem because almost half of the data records contain errors that could mess up processes.


Pros And Cons of AI In Manufacturing DataScienceCentral.com

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The fourth industrial revolution has been a game-changer, with the global economy's expansion driving the adoption of new technologies across sectors. Manufacturers are using AI software in product design, production, supply chain, and logistics. AI analytics and data are helping in improving product quality and efficiency. Advances in machine learning, artificial intelligence (AI), and Big Data have initiated an algorithm-based era. Today companies are able to automate multiple tasks, cutting down on errors as well as downtime and expenditures associated with them using AI.


Fujitsu turns attention to workflow management to lift employee experience

ZDNet

Since completing a degree in journalism, Aimee has had her fair share of covering various topics, including business, retail, manufacturing, and travel. She continues to expand her repertoire as a tech journalist with ZDNet. Fujitsu is usually the one providing the technology to help customers solve problems. But this time around, the Japanese conglomerate was the one having issues. As Fujitsu executive officer, EVP, and global services business group head Tim White described during the ServiceNow Knowledge 22 Sydney event on Wednesday, the company may be a global organisation, but it did not necessarily build out like one.


How AI can Transform Business Intelligence

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AI and machine learning are enabling businesses to pull out valuable insights that enable businesses to forecast industry trends and user behavior. That's the reason enterprises are eager to hire AI developers to upgrade solutions. Wondering how AI in business intelligence can be leveraged? Let's understand the undeniable potential of AI in business intelligence. Business intelligence's real potential can be gauged in breaking down a large volume of data into granular insights.


Access and Action: Healthcare Systems Put Big Data to Work

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Across all industries, organizations are now managing more data, nearly 14 petabytes on average, according to Dell Technologies' 2020 Global Data Protection Index (1 petabyte is just over 1 million gigabytes). In healthcare, providers and patients want to see more done with all that data. Some 75 percent of healthcare consumers want to work together with providers on wellness goals, according to Deloitte research, and 85 percent of physicians expect interoperability and data sharing to become standardized. The pandemic has highlighted the value of innovative technologies to gather, manage and gain insights from the vast stores of data that hospitals collect, guiding them toward improved care and adaptive clinical workflows. "The pandemic has been a huge validation of the path we were on and the investments we've made in data management," Lamm says.


It's a Marketing Mess! Artificial Intelligence vs Machine Learning

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There are many types of analytics that are used in the security world; some are defined by vendors, others by analysts. Let's begin by using the Gartner analytics maturity curve as a model for the list, with the insertion of one additional term slotted in the middle of the curve: Behavioral Analytics. Descriptive Analytics (Gartner): Descriptive Analytics is the examination of data or content, usually manually performed, to answer the question "What happened?" Baikalov explains that descriptive Analytics is the realm of a SIEM (Security Information and Event Management system) like ArcSight: "these systems gather and correlate all log data and report on known bad activities." Diagnostic Analytics (Gartner): Diagnostic Analytics is a form of advanced analytics which examines data or content to answer the question "Why did it happen?",