WEBINAR: Connected Intelligence Solutions with AI and ML - 11 December 2018

Data Science Central Webinar Series Event

Connected Intelligence Solutions with AI and ML Join us for this latest DSC Webinar on December 11th, 2018 TIBCO Connected Intelligence solutions for energy and utility companies provide powerful capabilities. Our platform connects data, systems, processes, and people—and it delivers predictive analytics, AI, and data visualisations for all aspects of asset management, customer information, distribution, forecasting, production, and supply chain.  The TIBCO platform can help you reduce costs and downtime, increase output, and improve customer retention. It lets you embed machine learning into sensors, processes, and equipment for modernised grids and smarter oil fields.  Speaker: Michael O'Connell, Chief Analytics Officer -- TIBCO Software, Inc. Hosted by: Bill Vorhies, Editorial Director -- Data Science Central Title:  Connected Intelligence Solutions with AI and ML Date:  Tuesday, December 11th, 2018 Time:  9:00 AM - 10:00 AM PST Space is limited so please register early: Reserve your Webinar seat now

Tips for protecting your data when losing an employee by Jason Park via @infomgmt

Most employers would be surprised to learn that departing internal employees can pose a much bigger threat to their business’s data security than external hackers.

These are really good guidelines. Some organisations take away access as soon as an employee tenders their resignation or at least limits it - however I would sound a small caution there - if someone is that keen to take a copy of data they will do that BEFORE they resign - so you have to have good auditing and great control over data transfers/data sticks in your office.

Understanding the new ePrivacy Regulation and how it differs from GDPR by Christian Auty via @infomgmt

The ePR is expected to address electronic communications, including text messages, email, chat applications and IoT devices. Think of the ePR as the traffic cop for data as it travels between controllers and processors governed by GDPR.

This is an insightful article by Christian that I think is a good high level analysis of the differences between the two.

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

Comparing the performance of machine learning models and algorithms using statistical tests and nested cross-validation by/via @rasbt

Sebastian Raschka compares the performance of machine learning models and algorithms using statistical tests and nested cross-validation.

This blog is great and very much worth a bookmark.  Go and look through the entire series of articles - this is useful bot both those new to data science and those who are experienced too.

WEBINAR: Transforming 3rd Party Data Into Actionable Insights - 28 November 2018

The rise of third party or external data has given data scientists and organisations additional building blocks to discover breakthrough insights. But many data scientists struggle to understand what third party data is relevant and struggle further to efficiently access and transform that data. In today’s Data Science Central webinar, we’ll explore innovative techniques to simplify third party data access and transformation. You will learn: Techniques for assessing third party data quality and relevance Strategies for accessing third party data Information about the third party data landscape as it applies to business outcomes Speakers: Mark Hookey, CEO -- DemystData Richard Scioli, General Manager, Platform -- DemystData Hosted by: Bill Vorhies, Editorial Director -- Data Science Central Title:  Transforming 3rd Party Data Into Actionable Insights Date:  Wednesday, November 28th, 2018 Time:  09:00 AM - 10:00 AM PST Space is limited so please register early: Reserve your Webinar seat now After registering you will receive a confirmation email containing information about joining the Webinar.

Managing risk in machine learning by Ben Lorica via @OReillyMedia

Machine learning models are becoming mission critical. Ben Lorica reveals data from a recent survey on ML adoption and discusses some important considerations for managing risk in machine learning.

This is really clear and easy to understand. A good place to start and it will give  you something to think about. Maybe it will give you something to consider in your own processes?