WEBINAR: AI Models And Active Learning - 4 December 2018

Data Science Central Webinar Series Event

AI Models And Active Learning Join us for this latest DSC Webinar on December 4th, 2018 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 organisations simply have too much data, posing a challenge for data scientists: unless at least some of that data is labelled, it's essentially useless for any ML approach that relies on supervised or semi-supervised learning. So, which data needs to be labelled? How much of a dataset needs to be labelled for an ML application to be viable? How can we solve the problem of having more data than we can reasonably analyse?  One promising answer is active learning. Active learning is unique in that it can both solve this data labelling 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: The pros and cons of active learning as an approach The three major categories of active learning How your active learner should decide which rows need labelling How to obtain those labels How to tell if active learning is appropriate for your ML project Speaker: Jennifer Prendki, VP of Machine Learning -- Figure Eight Hosted by: Bill Vorhies, Editorial Director -- Data Science Central   Title:  AI Models And Active Learning Date:  Tuesday, December 4th, 2018 Time:  9:00 AM - 10:00 AM PST   Space is limited so please register early: Reserve your Webinar seat now