Core Machine Learning and Data Science Techniques

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.

This half-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, 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