AltexSoft

MAY 5, 2023

AI in a nutshell Artificial Intelligence (AI) , at its core, is a branch of computer science that focuses on developing algorithms and computer systems capable of performing tasks that typically require human intelligence. Deep Learning is a subset of machine learning that focuses on building complex algorithms named deep neural networks.

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AltexSoft

FEBRUARY 21, 2023

This blog post will delve into the challenges, approaches, and algorithms involved in hotel price prediction. Hotel price prediction is the process of using machine learning algorithms to forecast the rates of hotel rooms based on various factors such as date, location, room type, demand, and historical prices.

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AltexSoft

JUNE 26, 2023

Note that in many cases, the process of gathering information never ends since you always need fresh data to re-train and improve existing ML models, gain consumer insights, analyze current market trends, and so on. Find sources of relevant data. Choose data collection methods and tools. Decide on a sufficient data amount.

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Data Collection Machine Learning Unstructured Data Non-relational Database 52

U-Next

SEPTEMBER 22, 2022

Several vetted models and algorithms are used in the predictive analytics tools in order to generate a large number of useful outcomes that are applicable to a wide range of use cases. . An evaluation of a sequence of data points over a period of time is carried out using this model. Types Of Predictive Models . Random Forest .

Hospitality 40

Hospitality Algorithm Machine Learning Banking 40

Edureka

AUGUST 2, 2023

Data processing analysts are experts in data who have a special combination of technical abilities and subject-matter expertise. They are essential to the data lifecycle because they take unstructured data and turn it into something that can be used.

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Knowledge Hut

DECEMBER 27, 2023

Many business owners and professionals are interested in harnessing the power locked in Big Data using Hadoop often pursue Big Data and Hadoop Training. What is Big Data? Big data is often denoted as three V’s: Volume, Variety and Velocity. Ecommerce businesses like Alibaba, Amazon use big data in a massive way.

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Big Data Tools Big Data Hadoop Database-centric 59

AltexSoft

SEPTEMBER 21, 2023

This endeavor would unwittingly plant the seeds for Elasticsearch, a technology that today drives data search and analytics for businesses around the globe. But like any technology, it has its share of pros and cons. As a result, Elasticsearch is exceptionally efficient in managing structured and unstructured data.

Engineering 52

Engineering NoSQL Programming Language Java 52

Tweag

APRIL 19, 2023

In particular oncology, a branch of medicine that focuses on cancer, could benefit immensely from these new technologies, which may enable clinicians to detect cancer earlier and increase chances of survival. A single hospital makes many captures a day, producing terabytes of such data to store and process.

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ProjectPro

FEBRUARY 8, 2017

.” said the McKinsey Global Institute (MGI) in its executive overview of last month's report: "The Age of Analytics: Competing in a Data-Driven World." 2016 was an exciting year for big data with organizations developing real-world solutions with big data analytics making a major impact on their bottom line.

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ProjectPro

AUGUST 24, 2021

But before you send out your resume for any data engineer job, and if you want to get shortlisted for further rounds, you need to have ample knowledge of various data engineering technologies and methods. Data Warehousing: Data warehousing utilizes and builds a warehouse for storing data.

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Data Engineering Data Engineer Coding Project 52

ProjectPro

MARCH 15, 2016

Hadoop is beginning to live up to its promise of being the backbone technology for Big Data storage and analytics. Companies across the globe have started to migrate their data into Hadoop to join the stalwarts who already adopted Hadoop a while ago. Hadoop allows us to store data that we never stored before.

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Hadoop Retail Healthcare Banking 40

AltexSoft

DECEMBER 15, 2021

On the surface, ML algorithms take the data, develop their own understanding of it, and generate valuable business insights and predictions — all without human intervention. It boosts the performance of ML specialists relieving them of repetitive tasks and enables even non-experts to experiment with smart algorithms.

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