Workshop – The Best of Predictive Analytics: Core Machine Learning and Data Science Techniques
Monday, June 4, 2018 in Las Vegas
Full day: 8:30am – 4:30pm
Room: Augustus I
Intended Audience: Interested in the fundamentals of modern machine learning techniques.
Knowledge Level: For this introductory-level workshop, it is helpful for attendees to already be familiar with the basics of probability and coding.
Companion Workshop: This workshop is the perfect complement for Dr. Elder’s other one-day PAW workshop, “The Deadly Dozen: The Top 12 Analytics Mistakes and the Techniques to Defeat Them,” although both workshops stand alone and may be taken in either order.
Workshop Description
This one-day session surveys standard and advanced methods for predictive modeling (aka machine learning).
Predictive analytics has proven capable of generating enormous returns across industries – but, with so many machine learning modeling methods, there are some tough questions that need answering:
- How do you pick the right one to deliver the greatest impact for your business, as applied over your data?
- What are the best practices along the way?
- How do you make it sure it works on new data?
In this workshop, renowned practitioner and hugely popular instructor Dr. John Elder will describe the key inner workings of leading machine learning algorithms, demonstrate their performance with business case studies, compare their merits, and show you how to select the method and tool best suited to each predictive analytics project.
Attendees will leave with an understanding of the most popular algorithms, including classical regression, decision trees, nearest neighbors, and neural networks, as well as breakthrough ensemble methods such as bagging, boosting, and random forests.
This workshop will also cover useful ways to visualize, select, reduce, and engineer features – such as principal components and projection pursuit. Most importantly, Dr. Elder reveals how the essential resampling techniques of cross-validation and bootstrapping make your models robust and reliable.
Throughout the workshop day, Dr. Elder will share his (often humorous) stories from real-world applications, highlighting mistakes to avoid.
If you’d like to become a practitioner of predictive analytics – or if you already are and would like to hone your knowledge across methods and best practices – this workshop is for you.
What you will learn:
- The tremendous value of learning from data
- How to create valuable predictive models with machine learning for your business
- Best Practices, with real-world stories of what happens when things go wrong
Why Attend?
View Dr. Elder describing his course, “The Best of Predictive Analytics,” in this brief video:
Schedule
- Workshop starts at 8:30am
- Morning Coffee Break at 10:30am – 11:00am
- Lunch provided at 12:30pm – 1:15pm
- Afternoon Coffee Break at 3:00pm – 3:30pm
- End of the Workshop: 4:30pm
Coffee breaks and lunch are included.
Free books:
Attendees receive a free copy of John Elder’s ebook Handbook of Statistical Analysis and
Data Mining Applications, a PDF of all training slides from this workshop, and an official certificate of completion at the conclusion of the workshop.
Special offer: Register for both this workshop as well as Dr. Elder’s other one-day PAW workshop, “The Deadly Dozen: The Top 12 Analytics Mistakes and the Techniques to Defeat Them” (complementary but not required), and also receive his co-authored book Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions (in Kindle form).
Instructor
Dr. John Elder, Founder and Chair, Elder Research
John Elder leads America’s most experienced Data Science consultancy. Founded in 1995, Elder Research has offices in Virginia, Washington DC, Maryland, and North Carolina. Dr. Elder co-authored books on data mining, ensembles, and text mining — two of which won book-of-the-year awards. John was a discoverer of ensemble methods, chairs international conferences, and is a popular keynote speaker. Dr. Elder is an (occasional) Adjunct Professor of Engineering at UVA, and was named by President Bush to serve 5 years on a panel to guide technology for national security.
