MACHIN ELEARNING IN RETAIL

Machine learning is a branch of computer science that is used to describe both explicitly programmed algorithms for supervised (prediction/classification) and unsupervised (clustering/feature detection) learning. Machine learning is a derived from the field of artificial intelligence (AI), which strives to provide computers with the ability to learn without being explicitly programmed.

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Wolfram|Alpha LLC

Wolfram|Alpha's long-term goal is to make all systematic knowledge immediately computable and accessible to everyone. We aim to collect and curate all objective data; implement every known model, method, and algorithm; and make it possible to compute whatever can be computed about anything. Our goal is to build on the achievements of science and other systematizations of knowledge to provide a single source that can be relied on by everyone for definitive answers to factual queries.

OTHER WHITEPAPERS
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Top 6 use cases for a self-sustainable Contact Center powered by Connected Data

whitePaper | August 10, 2022

Connected Data, though used synonymously with Big Data, is carefully created and curated for storage and reference during the customer’s lifetime. As we already know, enterprises need to carefully build and store Big Data to avoid creating a data swamp. Connected Data goes a step ahead of Big Data. It is customer-specific data built in a context-sensitive framework to ensure a 360-degree view of the customer profile, purchasing habits, preferences, etc., at a glance.

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Four Reasons Your Metadata Is Broken

whitePaper | April 1, 2021

Metadata is more important now than ever. New technologies have enabled businesspeople who have traditionally not been analysts to work with data. The consumerization of IT means people expect systems to be intuitive and require little training. With so many people using data to support so many kinds of decisions, it’s critical that your data is described, defined, and understood.

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6 best practices for cloud data integration

whitePaper | September 1, 2022

Organizations often adopt a hybrid cloud strategy when deploying their first analytical solution to the cloud. The cloud combines sheer infinite scalability with consumption-based pricing. These are desirable attributes for analytical environments that require scalability and can be very expensive when scaled for maximum capacity over an extended period.

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How to Analyze and Maximize Customer Retention: Asset

whitePaper | May 10, 2022

Understanding all the factors that impact customer health and retention requires a comprehensive view of complex data.

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Enterprise Data Orchestration

whitePaper | June 3, 2021

Data growth continues at an exponential rate even as cloud architectures make data management more complex and advanced applications necessitate more data movement. So what can be done to enable clean data capture and movement across an enterprise? Read this white paper to learn the requirements for data orchestration at scale and discover how you can build a holistic data architecture that enables successful DataOps.

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Building a High-Performance Data Organization

whitePaper | May 6, 2022

Every organization today recognizes the strategic value of generating actionable insights from their enterprise data.

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Spotlight

Wolfram|Alpha LLC

Wolfram|Alpha's long-term goal is to make all systematic knowledge immediately computable and accessible to everyone. We aim to collect and curate all objective data; implement every known model, method, and algorithm; and make it possible to compute whatever can be computed about anything. Our goal is to build on the achievements of science and other systematizations of knowledge to provide a single source that can be relied on by everyone for definitive answers to factual queries.

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