@ -140,7 +140,7 @@ Now that we have an idea of the relationship between the binary categories of co
> **🧮 Show Me The Math**
>
> Remember how linear regression often used ordinary least squares to arrive at a value? Logistic regression relies on the concept of 'maximum likelihood' using [sigmoid functions](https://wikipedia.org/wiki/Sigmoid_function). A 'Sigmoid Function' on a plot looks like an 'S' shape. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like thus:
> Remember how linear regression often used ordinary least squares to arrive at a value? Logistic regression relies on the concept of 'maximum likelihood' using [sigmoid functions](https://wikipedia.org/wiki/Sigmoid_function). A 'Sigmoid Function' on a plot looks like an 'S' shape. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like this:
@ -8,7 +8,7 @@ In Asia and India, food traditions are extremely diverse, and very delicious! Le
## What you will learn
In this section, you will build on the skills you learned in the first part of this curriculum all about regressionn to learn about other classifiers you can use that will help you learn about your data.
In this section, you will build on the skills you learned in the first part of this curriculum all about regression to learn about other classifiers you can use that will help you learn about your data.
> There are useful low-code tools that can help you learn about working with classification models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)