All Videos Tagged Machine Learning”, ML, (Data Science Central) - Data Science Central 2020-06-04T08:22:32Z https://www.datasciencecentral.com/video/video/listTagged?tag=Machine+Learning%E2%80%9D%2C+ML%2C&rss=yes&xn_auth=no DSC Webinar Series: Graph Algorithms Combined with ML are Saving the World tag:www.datasciencecentral.com,2020-04-30:6448529:Video:948778 2020-04-30T22:44:41.875Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-graph-algorithms-combined-with-ml-are-saving"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/4600406261?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Graph algorithms such as PageRank, community detection and similarity matching have moved from the classroom to the data scientist's and business analyst's toolkit. Organizations are gaining actionable insights by connecting and analyzing their data. <br></br> <br></br> Machine learning is an essential approach for getting the most insight out of… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-graph-algorithms-combined-with-ml-are-saving"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/4600406261?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Graph algorithms such as PageRank, community detection and similarity matching have moved from the classroom to the data scientist's and business analyst's toolkit. Organizations are gaining actionable insights by connecting and analyzing their data. <br /> <br /> Machine learning is an essential approach for getting the most insight out of your data. Machine learning enables data scientists and business users to scale their analysis and, when used in combination with graph algorithms, provides unprecedented insights that can have significant real-world impact.<br /> <br /> In this latest Data Science Central webinar, we will:<br /> <br /> Describe the five categories of graph algorithms: Shortest Path, Centrality, Community Detection, Similarity, and Classification;<br /> Explain how graphs provide an intuitive data model to improve the accuracy of supervised machine learning with new training data &amp; power explainable AI;<br /> Examine how in-database machine learning such as deep learning, community detection, and low rank approximation is possible with scalable native graph databases;<br /> Demonstrate how modern graph analytics tools can provide no-code data import, querying, feature extraction, and ML training.<br /> <br /> We will cover use cases and case studies for graph analytics and machine learning that include real-time fraud detection at four of the top five global banks, personalized offers for 300 million consumers and care path recommendations to improve the well being of 50 million patients. We will also share open source community initiatives that are leveraging graph analytics to analyze COVID-19 data.<br /> <br /> Speakers:<br /> Dr. Victor Lee, Head of Product Strategy and Developer Relations - TigerGraph<br /> Emma Liu, Senior Product Manager - TigerGraph<br /> <br /> Hosted by: <br /> Stephanie Glen, Editorial Director - Data Science Central DSC Webinar Series: ML vs Holt-Winters Forecasting tag:www.datasciencecentral.com,2020-01-15:6448529:Video:922800 2020-01-15T01:25:37.759Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-ml-vs-holt-winters-forecasting-1"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3819607266?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Machine Learning is all the rage, but when does it make sense to use it for forecasting? How do statistical forecasting methods compare?<br></br> <br></br> In this latest Data Science Central webinar, we will show you how the Holt-Winters forecasting algorithm works. Then we’ll use the HOLT_WINTERS() function with InfluxData to make our own time series… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-ml-vs-holt-winters-forecasting-1"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3819607266?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Machine Learning is all the rage, but when does it make sense to use it for forecasting? How do statistical forecasting methods compare?<br /> <br /> In this latest Data Science Central webinar, we will show you how the Holt-Winters forecasting algorithm works. Then we’ll use the HOLT_WINTERS() function with InfluxData to make our own time series forecast.<br /> <br /> <br /> Speaker:<br /> Anais Dotis-Georgiou, Developer Advocate - InfluxData<br /> <br /> Hosted by:<br /> Rafael Knuth, Contributing Editor - Data Science Central DSC Webinar Series: Data Mastering at Scale tag:www.datasciencecentral.com,2019-10-30:6448529:Video:903806 2019-10-30T00:48:10.189Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-data-mastering-at-scale"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3686726325?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Master data management (MDM) software turned 15 years old this year.<br></br> <br></br> Originally launched in 2004 by SAP, master data management systems aimed to help resolve the data unification problem by creating a central source of standardized references to customers, products, employees, suppliers, physical assets and other data across their many IT… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-data-mastering-at-scale"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3686726325?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Master data management (MDM) software turned 15 years old this year.<br /> <br /> Originally launched in 2004 by SAP, master data management systems aimed to help resolve the data unification problem by creating a central source of standardized references to customers, products, employees, suppliers, physical assets and other data across their many IT systems.<br /> <br /> MDM is valuable, but it’s also slow, labor-intensive, and costly. As the scale of MDM projects increases to millions of entities and hundreds or thousands of data sources, the traditional methods often fail.<br /> <br /> Mike Stonebraker will share his view on how MDM technology and MDM organizations must change to fulfill the promise of MDM at scale. In this latest Data Science Central webinar, we will review:<br /> <br /> Why large enterprises need data management solutions that solve data mastering challenges at scale<br /> Why traditional, rule-based, data mastering options are struggling to keep up<br /> How Machine Learning can be used to address large-scale data mastering challenges<br /> <br /> Speaker:<br /> Mike Stonebraker, CTO &amp; Co-Founder - Tamr, Inc.<br /> <br /> Hosted by:<br /> Stephanie Glen, Editorial Director - Data Science Central DSC Webinar Series: Clean Data & Accurate ML Models tag:www.datasciencecentral.com,2019-02-18:6448529:Video:803540 2019-02-18T23:02:30.627Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-clean-data-accurate-ml-models"><br /> <img alt="Thumbnail" height="0" src="https://storage.ning.com/topology/rest/1.0/file/get/1136933577?profile=original&amp;width=0&amp;height=0" width="0"></img><br /> </a> <br></br>Predictive analytics can provide your organization with data insights and differentiation to rise above the competition. However, Machine learning (ML) outcomes are only as good as the data they are built upon. Getting the data ready for accurate modeling is time consuming, cumbersome, and a waste of data professionals’ skills to be polishing the… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-clean-data-accurate-ml-models"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/1136933577?profile=original&amp;width=0&amp;height=0" width="0" height="0" alt="Thumbnail" /><br /> </a><br />Predictive analytics can provide your organization with data insights and differentiation to rise above the competition. However, Machine learning (ML) outcomes are only as good as the data they are built upon. Getting the data ready for accurate modeling is time consuming, cumbersome, and a waste of data professionals’ skills to be polishing the materials they rely on while they should focus on the work that matters—creating accurate predictions that improve products, services, and organizational efficiency.<br /> <br /> In this latest Data Science Central webinar, we will see how the data preparation process can be streamlined to produce an accurate model for Amazon SageMaker. Guest speaker Kris Skrinak, Machine Learning Segment Lead from Amazon Web Services Partner Network will provide deep insights.<br /> <br /> Join this webinar and you will learn:<br /> <br /> The modern scalable and agile cloud data pipeline for analytics and ML applications<br /> What it takes to deliver accurate models leveraging Amazon SageMaker<br /> Typical data flaws and how to remediate them with Trifacta data preparation solutions<br /> End to end demo from data acquisition, cleansing to feature engineering and modeling with Trifacta &amp; Amazon SageMaker<br /> <br /> Speakers:<br /> Vijay Balasubramaniam, Sr. Partner Solutions Architect - Trifacta<br /> <br /> Kris Skrinak, Machine Learning Segment Lead – Amazon Web Services<br /> <br /> Hosted by:<br /> Bill Vorhies, Editorial Director - Data Science Central DSC Webinar Series: Applying Convolutional Neural Networks with TensorFlow tag:www.datasciencecentral.com,2019-02-18:6448529:Video:803388 2019-02-18T22:57:13.644Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-applying-convolutional-neural-networks-with"><br /> <img alt="Thumbnail" height="0" src="https://storage.ning.com/topology/rest/1.0/file/get/1136854572?profile=original&amp;width=0&amp;height=0" width="0"></img><br /> </a> <br></br>In this latest Data Science Central Deep Learning Fundamentals Series webinar, we will cover the fundamentals behind TensorFlow and how to apply them within a convolutional neural network (CNN) example. The principles we will cover include CNN concepts and their impact to the accuracy and loss of your network.<br></br> <br></br> All these concepts… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-applying-convolutional-neural-networks-with"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/1136854572?profile=original&amp;width=0&amp;height=0" width="0" height="0" alt="Thumbnail" /><br /> </a><br />In this latest Data Science Central Deep Learning Fundamentals Series webinar, we will cover the fundamentals behind TensorFlow and how to apply them within a convolutional neural network (CNN) example. The principles we will cover include CNN concepts and their impact to the accuracy and loss of your network.<br /> <br /> All these concepts will be brought to life by demonstrating how Databricks simplifies deep learning - letting you quickly access ready-to-use ML environments, as well as prepare data, and train models faster. After this session, if requested, you will receive the presentation and associated notebooks so you can run the samples yourself.<br /> <br /> Speaker:<br /> Denny Lee, Technical Product Manager - Databricks<br /> <br /> Hosted by:<br /> Bill Vorhies, Editorial Director - Data Science Central