Discover how to solve value estimation problems with machine learning. Learn how to build a value estimation system that can estimate the value of a home.
Value estimationâone of the most common types of machine learning algorithmsâcan automatically estimate values by looking at related information. For example, a website can determine how much a house is worth based on the property's location and characteristics. In this project-based course, discover how to use machine learning to build a value estimation system that can deduce the value of a home. Follow Adam Geitgey as he walks through how to use sample data to build a machine learning model, and then use that model in your own programs. Although the project featured in this course focuses on real estate, you can use the same approach to solve any kind of value estimation problem with machine learning.
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
Choose the best features for home value prediction
Use as few features as possible: The curse of dimensionality