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Machine Learning and AI Foundations: Value Estimations

Mar 22, 2017 • Adam Geitgey

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About this course

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.



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Welcome

42s
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What you should know

21s
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Using the exercise files

31s
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Set up the development environment

2m 21s
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What is machine learning?

3m 11s
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Supervised machine learning for value prediction

2m 45s
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Build a simple home value estimator

2m 37s
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Find the best weights automatically

4m 7s
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Cool uses of value prediction

2m 6s
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Introduction to NumPy, scikit-learn, and pandas

1m 22s
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Think in vectors: How to work with large data sets efficiently

2m 58s
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The basic workflow for training a supervised machine learning model

2m 22s
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Gradient boosting: A versatile machine learning algorithm

3m 55s
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Explore a home value data set

2m 56s
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Standard conventions for naming training data

53s
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Decide how much data you need

2m 4s
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Feature engineering

4m 11s
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Choose the best features for home value prediction

3m 19s
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Use as few features as possible: The curse of dimensionality

1m 50s
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Prepare the features

1m 48s
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Training vs. testing data

1m 3s
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Train the value estimator

2m 51s
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Measure accuracy with mean absolute error

1m 31s
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Overfitting and underfitting

2m 44s
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The brute force solution: Grid search

2m 46s
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Feature selection

2m 26s
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Predict values for new data

2m 39s
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Retrain the classifier with fresh data

1m 48s
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Wrap-up

48s