Introduction

• Supervised machine learning and the technology boom
• Using the exercise files
• What you should know

1. Supervised Learning with Python

• What is supervised learning?
• Python supervised learning packages
• Predicting with supervised learning

2. Regression Modeling

• Defining logistic and linear regression
• Steps to prepare data for modeling
• Checking your dataset for assumptions
• Creating a linear regression model
• Creating a logistic regression model
• Evaluating regression model predictions

3. Decision Trees

• Identify common decision trees
• Splitting data and limiting decision tree depth
• How to build a decision tree
• Creating your first decision trees
• Analyzing decision tree performance
• Exploring how ensemble methods create strong learners

4. K-Nearest Neighbors

• Discovering your k-nearest neighbors
• What's the big deal about k
• How to assemble a KNN model
• Building your own KNN
• Deciphering KNN model metrics
• Searching for the best model

5. Neural Networks

• Biological vs. artificial neural networks
• Preprocessing data for modeling
• How neural networks find patterns in data
• Assembling your neural networks
• Comparing networks and selecting final models

Conclusion

• Ethical overview
• How can I keep developing my skills in supervised learning?