koalaverse/analyticssummit19

Material for 2019 Analytics Summit Machine Learning with R Training

Machine Learning with R

Course Description

Welcome to Machine Learning with R! This short course provides a hands-on approach to applying machine learning techniques with the R programming language. In this course you will gain deeper knowledge around the machine learning process and apply various supervised machine learning algorithms. The following is an outline of the material covered in this training:

Day 1

Topic	Time
Getting started	8:30 - 9:15
Supervised modeling process	9:30 - 10:30
Feature & target engineering	10:45 - 11:45
Lunch	12:00 - 1:00
Regression & cousins	1:00 - 2:30
Interpretable machine learning	2:45 - 4:15
Q&A	4:15 - 5:00

Day 2

Topic	Time
Recap & morning discussion	8:30 - 9:00
Tree-based methods	9:00 - 12:00
Lunch	12:00 - 1:00
Support vector machines	1:00 - 2:30
Stacked models & auto ML	2:45 - 3:15
Kaggle competition	3:30 - 4:30
Q&A	4:30 - 5:00

Schedule is still being refined and is subject to change; however, the topics should remain the same.

Course Preparation

To prepare for this course please complete the following prior to the day of class:

1. Download the student material here. This will provide you with the R scripts and notebooks to follow along during class.

2. After downloading the files, open the 00-run-this-script-first.R and run the code. This will make sure you have all required packages that will be used in the class.

3. All slides are available via the hyperlinks in the schedule above so that you can follow along.

4. Please have the following versions of R and RStudio installed. If you have an earlier version of R that is at least version 3.4.5 or later you should be ok but its best to be as current as possible.

   • R: 3.5.1 [download latest version]
   • RStudio: 1.1.463 [download latest version]

5. This course makes strong assumptions about your prior knowledge such as your ability to define functions, manage R objects, control the flow of a program, and other basic tasks. To ensure your success, be sure that you have reviewed and are comfortable with the material covered in the Intro to R and Intermediate R courses.

If you have any specific questions prior to the class you can reach out to Brad Boehmke at [email protected] or Brandon Greenwell at [email protected].