This is the first section of HarvardX’s Machine Learning course. This section focus on:
- Explaining the difference between the outcome and the features.
- Explaining when to use classification and when to use prediction.
- Explaining the importance of prevalence.
- Explaining the difference between sensitivity and specificity.
Comprehension Check: Introduction to Machine Learning
Q1
True or False: A key feature of machine learning is that the algorithms are built with data.
A. True
B. False
Q2
True or False: In machine learning, we build algorithms that take feature values (X) and train a model using known outcomes (Y) that is then used to predict outcomes when presented with features without known outcomes.
A. True
B. False