Darwin

  • [Webinar] Operationalizing Machine Learning: When Science Meets Returns55:36

    [Webinar] Operationalizing Machine Learning: When Science Meets Returns

    The use of machine learning in data-driven organizations has always been about acting upon knowledge to create a competitive advantage, not the science itself. In this webinar discover how automated

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    On-Demand Webinar: From Custom Dataset to ML Model in 30 Minutes

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  • From Data to Application: Darwin's Unique Approach to AutoML

    From Data to Application: Darwin's Unique Approach to AutoML

    Darwin automates time-consuming tasks ranging from model creation and optimization to model deployment and continuous maintenance.

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  • Garbage In, Garbage Out: Automated ML Begins with Quality Data

    Garbage In, Garbage Out: Automated ML Begins with Quality Data

    Machine learning methods are highly dependent on the quality of the data they receive as input, but data preparation and cleaning can be an unwieldy task, taking up 60% of data scientists' time.

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  • Breaking Away from Cookie-Cutter Algorithms: True Generalization with Evolutionary Methods

    Breaking Away from Cookie-Cutter Algorithms: True Generalization with Evolutionary Methods

    Most autoML solutions in the market focus on searching for the best algorithm to fit a given data set. However, these methods lack the ability to produce novel, elegant model architectures.

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  • How to Put Machine Learning Models to Work: Bridging the Gap Between Model Production and Operationalization

    How to Put Machine Learning Models to Work: Bridging the Gap Between Model Production and Operationalization

    Automated machine learning has the potential to reduce the burden on overwhelmed teams by automating the bottlenecks in the data science process. But just having an algorithm isn't enough.

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  • Darwin: Automate Model Building1:36

    Darwin: Automate Model Building

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  • Lending Beyond Credit Scores Webinar33:45

    Lending Beyond Credit Scores Webinar

    Learn how machine learning systems can help lenders identify and track more variables to build a more accurate picture of a customer's financial state. The video goes through practical examples of how

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  • Fantasy Football Week #2 Recap: Your Machine Learning Cheat Sheet

    Fantasy Football Week #2 Recap: Your Machine Learning Cheat Sheet

    We hope your fantasy league's first week was spectacular. If not, don’t lose hope; even the 2010 Seattle Seahawks made the playoffs with a losing record. Even better, you have Darwin on your team!

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  • Fantasy Football Week #1 Recap: Your Machine Learning Cheat Sheet

    Fantasy Football Week #1 Recap: Your Machine Learning Cheat Sheet

    Football season couldn’t come fast enough, and now you can’t wait to pummel your office buddies with a stacked roster under your mildly inappropriate team name. However, the competition is fierce...

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  • The Darwin Difference: Why Darwin Stands Out From the AutoML Pack

    The Darwin Difference: Why Darwin Stands Out From the AutoML Pack

    Darwin™️ is an automated model building product that allows you to go from data to model in less time than traditional methods, enabling the rapid prototyping of scenarios and extraction of insights.

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  • From Data Professional to Data Legend with Machine Learning28:34

    From Data Professional to Data Legend with Machine Learning

    Watch this on-demand webinar to learn how to harness the power of machine learning so you can move from data to results at a staggering speed. During this quick 30 minutes the SparkCognition team d

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  • Neuroevolution-based Automated Model Building59:21

    Neuroevolution-based Automated Model Building

    Common neural network architectures may work well for known, established data problems; however, they can fall short when modern machine learning applications demand more performance and higher levels

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  •  Darwin the Tailor: Get Custom Models for Your Data1:30

    Darwin the Tailor: Get Custom Models for Your Data

    Darwin does the hard work of creating custom models by automating the task and quickly converting your data into usable resources for your industry.

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  • Use Case: A Full Framework for Automated Loan Processing

    Use Case: A Full Framework for Automated Loan Processing

    With AMB, lenders can expect an increase in loans offered, with optimized interest pricing and lower defaults, and a forecasted 20% ROI.

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  • Customer Success Story: Predicting Customer Complaints for Telecom

    Customer Success Story: Predicting Customer Complaints for Telecom

    With the power of NLP and auto ML a major telecom provider expects to reduce complaint call volume by 33%, all while increasing brand loyalty.

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  • Darwin Demo: Fraud Detection4:24

    Darwin Demo: Fraud Detection

    This video shows how Darwin, an automated model building tool, empowers data scientists by using historical data sets to build a model that detects fraudulent transactions.

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  • Darwin Demo: Predicting Insurance Pricing3:40

    Darwin Demo: Predicting Insurance Pricing

    This video shows how Darwin, an automated model building tool, empowers data scientists by using historical data sets to build a model that predicts insurance pricing.

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  • Darwin Demo: Predicting Customer Churn4:10

    Darwin Demo: Predicting Customer Churn

    This demo shows how Darwin creates a machine learning model to predict customer churn.

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  • Darwin Demo: Automated Loan Approval3:56

    Darwin Demo: Automated Loan Approval

    This video shows how Darwin, an automated model building tool, empowers data scientists to build a model that lending institutions can use to understand their customer base and better price loans.

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  • Use Case: Preventing Customer Churn for Retail Banks

    Use Case: Preventing Customer Churn for Retail Banks

    As competition, banks are expected to lose revenue due to customer churn. Natural language processing and automated machine learning generate insights into why customers churn and how to retain them.

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