Think in vectors: How to work with large data sets efficiently

Welcome

What you should know

Using the exercise files

Set up the development environment

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

Introduction to NumPy, scikit-learn, and pandas

The basic workflow for training a supervised machine learning model

Gradient boosting: A versatile machine learning algorithm

Explore a home value data set

Standard conventions for naming training data

Decide how much data you need

Feature engineering

Choose the best features for home value prediction

Use as few features as possible: The curse of dimensionality

Prepare the features

Training vs. testing data

Train the value estimator

Measure accuracy with mean absolute error

Overfitting and underfitting

The brute force solution: Grid search

Feature selection

Predict values for new data

Retrain the classifier with fresh data

Wrap-up