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, and anyone else interested in getting started with practical machine learning.
- No experience with R required