basics of machine learning
TRANSCRIPT
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ObjectiveEntice you to want to go learn more about machine learning.
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Data is huge. People are expensive. Computation is cheap.
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Use applied statistics to let the computers program themselves.
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Types
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Types
Supervised "I have a set of examples with the right answers, I want to learn a pattern and use it on examples where I don't have the answers."
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Types
Unsupervised "I have data with no answers, but I want to find a pattern that might lead me to an answer."
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TypesReinforcement "I want to start with what I know now, and be able to learn new things as they come along."
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Types
Supervised "I have a set of examples with the right answers, I want to learn a pattern and use it on examples where I don't have the answers."
Unsupervised "I have data with no answers, but I want to find a pattern that might lead me to an answer."
Reinforcement "I want to start with what I know now, and be able to learn new things as they come along."
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Types
Supervised "I have a set of examples with the right answers, I want to learn a pattern and use it on examples where I don't have the answers."
Unsupervised "I have data with no answers, but I want to find a pattern that might lead me to an answer."
Reinforcement "I want to start with what I know now, and be able to learn new things as they come along."
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Regression vs Classification
Supervised Learning
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RegressionUse continuous data to make a model that predicts where new data will fit.
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ClassificationLabel data into "buckets", and make predictions on which bucket a new data point will fall into.
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Where to learn more
Books
Podcasts
Interactive
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Where to learn more
Books
Podcasts
Interactive
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Where to learn more
Books
Podcasts
Interactive
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Where to learn more
Books
Podcasts
Interactive
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Where to learn more
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