Hands-On: Machine Learning with Python Made Easy—No, Really!

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 course, you will be provided all the fundamentals for understanding and effectively training predictive models using the mighty random forest algorithm. Focusing on core concepts and intuitions means that no complicated math is required.

With this understanding, you'll be equipped to tackle the next topics: feature importance and feature engineering. We'll wrap up the day by discussing how to determine if your model is any good. Lastly, you will be given 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
• How to evaluate your random forest for accuracy
• Feature importance and feature engineering using random forests
• How to analyze your feature engineering efforts—did they work?
• Additional resources to extend your learning

Geared To

• Business and data analysts
• BI and analytics developers and managers
• Business users
• Aspiring data scientists
• Anyone interested in learning to analyze their business data visually

No skills in advanced mathematics or statistics are required!