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This course has been a broad overview of machine learning. We looked at a lot of big ideas and we even explored some code. Let's do a quick review.
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This course has been a broad overview of machine learning.
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We looked at a lot of big ideas and we even exported some code.
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Let's do a quick review.
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Machine Learning is a new approach to artificial intelligence.
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With an emphasis on statistical analysis, and the ability for the computer to write
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its own set of rules, rather than humans writing all of the conditional logic.
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There are two major categories of Machine Learning, Supervised Learning and
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Unsupervised Learning.
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Supervised learning is when a machine intelligence predicts a category or
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a quantity using models of classification and regression respectively.
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Unsupervised learning is when a computer analyzes unlabeled data and
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attempts to recognize patterns.
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The most common unsupervised models use
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clustering to group similar things together.
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When handling data, remember that an example is a single element in a dataset.
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And a feature is one characteristic of an example.
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These basic models can lead to more complex and emerging behaviors in specific
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domains like chatbots, image recognition, speech recognition and more.
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More specialized applications like this can be served by higher level machine
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learning platforms including AWS and IBM Watson.
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If you didn't understand everything, don't worry.
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That's normal.
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I encourage you to watch this course again, explore the teacher notes,
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and look at the documentation for scikit-learn.
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You may also want to try exploring more problems using scikit-learn.
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See if you can apply classification to another data set.
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Or try something more advanced like regression or clustering.
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With more practice and
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repetition, your understanding will grow and you'll be ready for more.
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