What is machine learning?

Learn the fundamentals of machine learning and build a random forest model.

Learning objectives

At the end of this unit you should be able to

• understand the basic concepts in machine learning,
• prepare your data for machine learning,
• train a random forest model,
• make predictions with the model,
• tune your model, and
• validate your model.

Machine learning kick off

To get an overview about the large field called “machine learning”, you can watch the videos below or do some internet research on your own. There are some great tutorials out there.

1. A Gentle Introduction to Machine Learning (12:44)
2. Decision Trees (17:21)
3. Decision Trees Part 2 - Feature Selection and Missing Data (5:15)
4. Random Forests Part 1 - Building, Using and Evaluating (9:53)
5. Random Forests Part 2 - Missing data and clustering (11:52)
6. Random Forests in R (15:09)
7. Machine Learning Fundamentals - Cross Validation (6:04)

Try to answer the following questions:

• What is machine learning?
• What is a decision tree?
• How does random forest work?
• What is a cross validation?

Comments?

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