Dive deeper into machine learning with our interactive machine learning intermediate course. You'll learn additional algorithms such as logistic regression and k-means clustering. You'll also learn about things like how to detect overfitting and the bias-variance tradeoff.

Then you’ll dig into understanding model performance using sensitivity and specificity as it relates to classification models. You'll get an introduction to clustering, an unsupervised learning technique designed to find patterns in data and group data into clusters that are closely related. And you'll discover the difference between supervised and unsupervised learning, as well as when it makes sense to use each type of machine learning.

At the end of the course, you'll complete a project using different machine learning techniques to predict the price of the stock market using data from the S&P500 index. This project is a chance for you to combine the skills you learned in this course and practice the machine learning workflow. It could make a good portfolio project to show future employers, and who knows, if your machine learning model is good enough, it might even make you some money in the stock market!

By the end of this course, you'll be able to:

## Learn Intermediate Machine Learning Techniques

### Logistic Regression

Learn the basics of logistic regression and classification.

### Introduction to Evaluating Binary Classifiers

Learn how to evaluate a classification model.

### Multiclass Classification

Learn how to use logistic regression with multiple categories.

### Overfitting

Learn how to detect overfitting and about the bias-variance tradeoff.

### Clustering Basics

Learn how to use clustering to group senators using voting patterns.

### K-Means Clustering

Learn to create and interpret scatter plots to explore relationships between variables.

### Predicting the Stock Market

Use machine learning techniques to predict the price of the SP500.