Workshop – Machine Learning with R: A Hands-On Introduction

Friday, June 24, 2022 – Caesars Palace, Las Vegas

Full-day: 8:00am – 4:00pm PDT

Intended Audience: People who want to use R to make predictions and discover valuable relationships in their data.

Knowledge Level: An introductory knowledge of R and machine learning is helpful, but not required.

 

Workshop Description

R offers a wide variety of machine learning (ML) functions, each of which works in a slightly different way. This one-day, hands-on workshop starts with ML basics and takes you step-by-step through increasingly complex modeling styles. This workshop makes ML modeling easier through the use of packages that standardize the way the various functions work. When finished, you should be able to use R to apply the most popular and effective machine learning models to make predictions and assess the likely accuracy of those predictions.

The instructor will guide attendees on hands-on execution with R, covering:

  • A brief introduction to R’s tidyverse functions, including a comparison of the caret and parsnip packages
  • Pre-processing data
  • Selecting variables
  • Partitioning data for model development and validation
  • Setting model training controls
  • Developing predictive models using naïve Bayes, classification and regression trees, random forests, gradient boosting machines, and neural networks (more, if time permits)
  • Evaluating model effectiveness using measures of accuracy and visualization
  • Interpreting what “black-box” models are doing internally

Hardware: Bring Your Own Laptop
Each workshop participant is required to bring their laptop.

Schedule

  • Workshop starts at 8:00am PDT
  • AM Break from 9:30 – 9:45am PDT
  • Lunch Break from 11:30 – 12:15am PDT
  • PM Break: 1:45 – 2:00pm PDT
  • Workshops ends at 4:00pm PDT

Instructor

Jared P. Lander, Chief Data Scientist, Lander Analytics

Jared P. Lander is Chief Data Scientist of Lander Analytics, the Organizer of the New York Open Statistical Programming Meetup​ and the New York​ and Government​ R Conferences​, an Adjunct Professor at Columbia Business School​, and a Visiting Lecturer at Princeton University​. With a masters from Columbia University​ in statistics and a bachelors from Muhlenberg College​ in mathematics, he has experience in both academic research and industry. Jared oversees the long-term direction of the company and acts as Lead Data Scientist, researching the best strategy, models and algorithms for modern data needs. This is in addition to his client-facing consulting and training. He specializes in data management, multilevel models, machine learning, generalized linear models, data management, visualization and statistical computing. He is the author of R for Everyone (now in its second edition), a book about R Programming geared toward Data Scientists and Non-Statisticians alike. The book is available from Amazon, Barnes & Noble and InformIT. The material is drawn from the classes he teaches at Columbia and is incorporated into his corporate training. Very active in the data community, Jared is a frequent speaker​ at conferences, universities and meetups around the world.