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re-numbering lesson groups
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jlooper committed Jun 4, 2021
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2 changes: 1 addition & 1 deletion Web-App/README.md → 3-Web-App/README.md
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Expand Up @@ -4,7 +4,7 @@ In this section of the curriculum, you will be introduced to an applied ML topic

## Lessons

1. [Build a Web App](1-Web-App/README.md)
1. [Build a Web App](3-Web-App/README.md)

## Credits

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4 changes: 3 additions & 1 deletion Classification/README.md → 4-Classification/README.md
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Expand Up @@ -10,4 +10,6 @@ In this section of the curriculum you will learn about how to classify data usin
4. [Applied ML: Build a Web App](4-Applied/README.md)
## Credits

"Getting Started with Classification" was written with ♥️ by [Cassie Breviu](@cassieview)
"Getting Started with Classification" was written with ♥️ by [Cassie Breviu](https://www.twitter.com/cassieview) and [Jen Looper](https://www.twitter.com/jenlooper)

The delicious recipes dataset was sourced from [Kaggle](https://www.kaggle.com/hoandan/asian-and-indian-cuisines)
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Expand Up @@ -11,7 +11,7 @@ One of the major consumers of classical machine learning models is the finance i

### Credit card fraud detection

We learned about [k-means clustering](Clustering/2-K-Means/README.md) earlier in the course, but how can it be used to solve problems related to credit card fraud?
We learned about [k-means clustering](5-Clustering/2-K-Means/README.md) earlier in the course, but how can it be used to solve problems related to credit card fraud?

K-means clustering comes in handy during a credit card fraud detection technique called **outlier detection**. Outliers, or deviations in observations about a set of data, can tell us if a credit card is being used in a normal capacity or if something unusual is going on. As shown in the paper below, you can sort credit card data using a k-means clustering algorithm and assign each transaction to a cluster based on how much of an outlier it appears to be. Then, you can evaluate for riskiest cluster for fraudulent versus legitimate transactions.

Expand All @@ -20,7 +20,7 @@ https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.680.1195&rep=rep1&type

In wealth management, an individual or firm handles investments on behalf of their clients. Their job is to sustain and grow wealth in the long-term, so it is essential to choose investments that perform well.

One way to evaluate how a particular investment performs is through statistical regression. [Linear regression](Regression/1-Tools/README.md) is a valuable tool for understanding how a fund performs relative to some benchmark. We can also deduce whether or not the results of the regression are statistically significant, or how much they would affect a client's investments. You could even further expand your analysis using multiple regression, where additional risk factors can be taken into account. For an example of how this would work for a specific fund, check out the paper below on evaluating fund performance using regression.
One way to evaluate how a particular investment performs is through statistical regression. [Linear regression](2-Regression/1-Tools/README.md) is a valuable tool for understanding how a fund performs relative to some benchmark. We can also deduce whether or not the results of the regression are statistically significant, or how much they would affect a client's investments. You could even further expand your analysis using multiple regression, where additional risk factors can be taken into account. For an example of how this would work for a specific fund, check out the paper below on evaluating fund performance using regression.

http://www.brightwoodventures.com/evaluating-fund-performance-using-regression/

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52 changes: 26 additions & 26 deletions README.md

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