All Videos Tagged Jennifer Prendki”, (Data Science Central) - Data Science Central 2020-07-11T12:33:17Z https://www.datasciencecentral.com/video/video/listTagged?tag=Jennifer+Prendki%E2%80%9D%2C&rss=yes&xn_auth=no DSC Webinar Series: AI Models And Active Learning tag:www.datasciencecentral.com,2018-12-04:6448529:Video:783199 2018-12-04T21:32:19.003Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-ai-models-and-active-learning"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/2781531614?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>The increased availability of computer resources and the prevalence of high-quality training data combined with smart learning schemas, have resulted in a rise in successful AI deployments. However, many organizations simply have too much data, posing a challenge for data scientists: unless at least some of that data is labeled, it's essentially useless… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-ai-models-and-active-learning"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/2781531614?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />The increased availability of computer resources and the prevalence of high-quality training data combined with smart learning schemas, have resulted in a rise in successful AI deployments. However, many organizations simply have too much data, posing a challenge for data scientists: unless at least some of that data is labeled, it's essentially useless for any ML approach that relies on supervised or semi-supervised learning. So, which data needs to be labeled? How much of a dataset needs to be labeled for an ML application to be viable? How can we solve the problem of having more data than we can reasonably analyze?<br /> <br /> One promising answer is active learning. Active learning is unique in that it can both solve this data labeling crisis and train models to be more accurate with less data overall. Join us for this latest Data Science Central webinar where we’ll cover:<br /> <br /> The pros and cons of active learning as an approach<br /> The three major categories of active learning<br /> How your active learner should decide which rows need labeling<br /> How to obtain those labels<br /> How to tell if active learning is appropriate for your ML project<br /> <br /> Speaker:<br /> Jennifer Prendki, VP, Machine Learning - Figure Eight<br /> <br /> Hosted by:<br /> Bill Vorhies, Editorial Director - Data Science Central DSC Webinar Series: The Essentials of Training Data for Machine Learning tag:www.datasciencecentral.com,2018-09-26:6448529:Video:763549 2018-09-26T22:09:35.844Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-the-essentials-of-training-data-for-machine"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/2781544165?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>A machine learning algorithm isn’t worth much without great training data to power it. After all, algorithms learn from data, discovering relationships, developing understanding, making decisions, and evaluating their confidence from the training data they’re given. And the better the training data is, the better the model performs. In fact,… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-the-essentials-of-training-data-for-machine"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/2781544165?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />A machine learning algorithm isn’t worth much without great training data to power it. After all, algorithms learn from data, discovering relationships, developing understanding, making decisions, and evaluating their confidence from the training data they’re given. And the better the training data is, the better the model performs. In fact, the quality and quantity of your training data has as much to do with the success of your data project as the algorithms themselves.<br /> <br /> Join us for this latest Data Science Central webinar on the basics of training data where we will cover:<br /> <br /> What training data is and why it’s so important<br /> What training data looks like for a variety of projects<br /> Why training data should be labeled and how to get it labeled<br /> How much training data you need<br /> <br /> Speaker: Jennifer Prendki, VP of Machine Learning -- Figure Eight<br /> <br /> Hosted by: Bill Vorhies, Editorial Director -- Data Science Central