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TDWI Hands-on Skills for The Business Analyst Seminar

virtual seminars

Hands-on Machine Learning Made Easy—No, Really!

April 14, 2021

9:00 am - 5:00 pm

Duration: Full Day Course

Central Time CT

Prerequisite: None

David Langer

Founder

Dave on Data

Course Outline

Getting started with advanced analytics and machine learning can be intimidating. Where do I start? How much math do I need? What algorithms should I use? The good news is that getting started with machine learning is easy—no really, it is!

In this hands-on tutorial we will provide all the fundamentals for understanding and effectively training predictive models using the mighty random forest algorithm (our personal favorite). Focusing on core concepts and intuitions means that no complicated math is required, and the R code will be a breeze, even for first-timers.

With this understanding you'll be equipped to tackle the next topics: data analysis and feature engineering. We'll wrap up the day discussing how to determine if your model is any good. Lastly, we want to send you off with a road map of suggested topics to take on as you continue your data science journey.

You Will Learn

  • Machine learning to solve classification problems
  • The decision tree algorithm—including pros and cons
  • The mighty random forest algorithm and how it "fixes" decision trees
  • Testing your random forest for accuracy
  • Data analysis and feature engineering using random forests
  • Analyzing your feature engineering efforts—did they work?
  • Additional resources to extend your learning

Geared To

  • Business/data analysts, database developers, BI/report developers, and anyone else interested in getting started with practical machine learning
  • No experience with R required

Computer Setup

Students will do labs on their own computers.

Participants should download and install the following prior to the event:

  • R 4.0.3 or higher (https://cran.rstudio.com/)
  • RStudio 1.3.1093 or higher (https://www.rstudio.com/products/rstudio/download/)
  • randomForest package (run install.packages(“randomForest”) from R command line)

Instructions will be emailed to registrants prior to the event to prepare your computer BEFORE the seminar.

There is no time allotted in class for computer preparation.

* Sessions are limited to 30 students. Register now!

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