Level: Beginner to Intermediate
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/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
- Business/data analysts
- Database developers
- BI/report developers
- Anyone else interested in getting started with practical machine learning
No experience with R required.
In this session, the instructor will teach you principles and practices, show you how to use the tools, and demonstrate with live examples. You will receive installation instructions and take-home workshop materials to complete hands-on exercises on your own, after the live session.
Completion of take-home workshop exercises will require R 3.5.3 or higher, RStudio 1.2.1335 or higher, and the randomForest package. Download and installation instructions will be provided.
TDWI LIVE STUDIO AUDIENCE:
This session will be recorded for development of an online learning course which will subsequently be available for purchase directly or via subscription. By attending, you agree that your likeness may appear in the online course, including audio and video.