In the linear regression course, we explored a supervised machine learning technique called linear regression. Linear regression works well when the target column we're trying to predict, the dependent variable, is ordered and continuous. If the target column instead contains discrete values, then linear regression isn't a good fit.

In this lesson, we'll focus on a classification technique called logistic regression. While a linear regression model outputs a real number as the label, a logistic regression model outputs a probability value. In binary classification, if the probability value is larger than a certain threshold probability, we assign the label for that row to `1` or `0` otherwise. To grasp the concepts of classification, we'll try to predict whether an applicant will be admitted to a graduate program in a U.S university.

You will learn logistic regression concepts such as the logistic function, as well as how to train a logistic regression model using scikit-learn's LogisticRegression class and how to use your model to predict the distinct classes for each value in a set of inputs.

As you work through each concept, you’ll get to apply what you’ve learned from within your browser — there's no need to use your own machine to do the exercises. The Python environment inside of this course includes answer checking so you can ensure that you've fully mastered each concept before learning the next.

#### Objectives

#### Lesson Outline

1. Classification

2. Introduction to the data

3. Logistic regression

4. Logistic function

5. Training a logistic regression model

6. Plotting probabilities

7. Predict labels

8. Next steps

9. Takeaways