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LinkedIn Learning

Machine Learning and AI Foundations: Value Estimations

via LinkedIn Learning

Overview

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 estimation—one of the most common types of machine learning algorithms—can 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.

Syllabus

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

Taught by

Adam Geitgey

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