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Reworking of 02-regression lesson #47

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Reworking of 02-regression lesson #47

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7893b87
update to regression lesson
mike-ivs Oct 28, 2024
51d78e8
minor format fixes and placeholder
mike-ivs Oct 28, 2024
7be4622
Add in polynomial and spline models to regression
mike-ivs Oct 28, 2024
001ce15
Merge branch 'carpentries-incubator:gh-pages' into gh-pages
mike-ivs Dec 5, 2024
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8 changes: 5 additions & 3 deletions _episodes/01-introduction.md
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Expand Up @@ -7,8 +7,8 @@ questions:
- "What are some useful machine learning techniques?"
objectives:
- "Gain an overview of what machine learning is and the techniques available."
- "Understand how machine learning and artificial intelligence differ."
- "Be aware of some caveats when using Machine Learning."
- "Understand how machine learning, deep learning, and artificial intelligence differ."
- "Be aware of some caveats when using machine mearning."

keypoints:
- "Machine learning is a set of tools and techniques that use data to make predictions."
Expand All @@ -19,7 +19,9 @@ keypoints:

# What is machine learning?

Machine learning is a set of techniques that enable computers to use data to improve their performance in a given task. This is similar in concept to how humans learn to make predictions based upon previous experience and knowledge. Machine learning encompasses a wide range of activities, but broadly speaking it can be used to: find trends in a dataset, classify data into groups or categories, make predictions based upon data, and even "learn" how to interact with an environment when provided with goals to achieve.
Machine learning is a set of techniques that enable computers to use data to improve their performance in a given task. This is similar in concept to how humans learn to make predictions based upon previous experience and knowledge. Machine learning is "data-driven", meaning that it uses the underlying statistics of a set of data to achieve a task.

Machine learning encompasses a wide range of tasks and activities, but broadly speaking it can be used to: find trends in a dataset, classify data into groups or categories, make predictions based upon data, and even "learn" how to interact with an environment when provided with goals to achieve.

### Artificial intelligence vs machine learning

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