What Is Machine Learning?
Learn how computers use data to “learn” and make predictions, and explore the difference between supervised and unsupervised learning, how models are trained and tested, and how we measure whether those models are good or not. You’ll also learn how models can go wrong if they’re either too simple or too complex—and how to spot those problems. This text is part of the Decision-Making Using Machine Learning Basics pathway.
Topics
- ethical issues in machine learning
- machine learning lifecycle
- clustering
- linear regression
- cubic regression
- model accuracy
- training and testing
- bias variance
- overfitting
- underfitting
- classification metrics
- regression accuracy
- MAE
- MSE
- RMSE
- supervised learning
- unsupervised learning
- predictive modeling
- model evaluation
- data science fundamentals