Machine Learning and AI Foundations: Value Estimations

سرفصل های دوره

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.

01 - Introduction

01 - Welcome

02 - What you should know

03 - Using the exercise files

04 - Set up the development environment

02 - 1. What Is Machine Learning and Value Prediction

01 - What is machine learning

02 - Supervised machine learning for value prediction

03 - Build a simple home value estimator

04 - Find the best weights automatically

05 - Cool uses of value prediction

03 - 2. An Overview of Building a Machine Learning System

01 - Introduction to NumPy, scikit-learn, and pandas

02 - Think in vectors How to work with large data sets efficiently

03 - The basic workflow for training a supervised machine learning model

04 - Gradient boosting A versatile machine learning algorithm

04 - 3. Training Data

01 - Explore a home value data set

02 - Standard conventions for naming training data

03 - Decide how much data you need

05 - 4. Features

01 - Feature engineering

02 - Choose the best features for home value prediction

03 - Use as few features as possible The curse of dimensionality

06 - 5. Coding Our System

01 - Prepare the features

02 - Training vs. testing data

03 - Train the value estimator

04 - Measure accuracy with mean absolute error

07 - 6. Improving Our System

01 - Overfitting and underfitting

02 - The brute force solution Grid search

03 - Feature selection

08 - 7. Using the Estimator in a Real-World Program

01 - Predict values for new data

02 - Retrain the classifier with fresh data

09 - Conclusion

01 - Wrap-up

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تولید کننده: LinkedIn Learning (Lynda)