Machine Learning and AI Foundations: Value Estimations

Introduction

• Welcome
• What you should know
• Using the exercise files
• Set up the development environment

1. What Is Machine Learning and Value Prediction?

• What is machine learning?
• Supervised machine learning for value prediction
• Build a simple home value estimator
• Find the best weights automatically
• Cool uses of value prediction

2. An Overview of Building a Machine Learning System

• Introduction to NumPy, scikit-learn, and pandas
• Think in vectors: How to work with large data sets efficiently
• The basic workflow for training a supervised machine learning model
• Gradient boosting: A versatile machine learning algorithm

3. Training Data

• Explore a home value data set
• Standard conventions for naming training data
• Decide how much data you need

4. Features

• Feature engineering
• Choose the best features for home value prediction
• Use as few features as possible: The curse of dimensionality

5. Coding Our System

• Prepare the features
• Training vs. testing data
• Train the value estimator
• Measure accuracy with mean absolute error

6. Improving Our System

• Overfitting and underfitting
• The brute force solution: Grid search
• Feature selection

7. Using the Estimator in a Real-World Program

• Predict values for new data
• Retrain the classifier with fresh data

Conclusion

• Wrap-up