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Supervised Learning


In this course, you'll learn about different types of supervised learning and how to use them to solve real-world problems.

In this course, you'll learn about different types of supervised learning and how to use them to solve real-world problems.


3 weeks

Real-world Projects

Completion Certificate

Last Updated June 9, 2023

Skills you'll learn:
Naive bayes classifiers • Model evaluation • Support vector machines • Decision trees
Basic descriptive statistics • Basic probability • Intermediate Python

Course Lessons

Lesson 1

Introduction to Supervised Learning

Before diving into the many algorithms of machine learning, it is important to take a step back and understand the big picture associated with the entire field.

Lesson 2

Linear Regression

Linear regression is one of the most fundamental algorithms in machine learning. In this lesson, learn how linear regression works!

Lesson 3

Perceptron Algorithm

The perceptron algorithm is an algorithm for classifying data. It is the building block of neural networks.

Lesson 4

Decision Trees

Decision trees are a structure for decision-making where each decision leads to a set of consequences or additional decisions.

Lesson 5

Naive Bayes

Naive Bayesian Algorithms are powerful tools for creating classifiers for incoming labeled data. Specifically Naive Bayes is frequently used with text data and classification problems.

Lesson 6

Support Vector Machines

Support vector machines are a common method used for classification problems. They have been proven effective using what is known as the 'kernel' trick!

Lesson 7

Ensemble Methods

Bagging and boosting are two common ensemble methods for combining simple algorithms to make more advanced models that work better than the simple algorithms would on their own.

Lesson 8

Model Evaluation Metrics

Learn the main metrics to evaluate models, such as accuracy, precision, recall, and more!

Lesson 9

Training and Tuning

Learn the main types of errors that can occur during training, and several methods to deal with them and optimize your machine learning models.

Lesson 10 • Project

Finding Donors Project

You've covered a wide variety of methods for performing supervised learning -- now it's time to put those into action!

Taught By The Best

Photo of Josh Bernhard

Josh Bernhard

Staff Data Scientist

Josh has been sharing his passion for data for over a decade. He's used data science for work ranging from cancer research to process automation. He recently has found a passion for solving data science problems within marketplace companies.

Photo of Luis Serrano

Luis Serrano


Luis was formerly a Machine Learning Engineer at Google. He holds a PhD in mathematics from the University of Michigan, and a Postdoctoral Fellowship at the University of Quebec at Montreal.

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Demonstrate proficiency with practical projects

Projects are based on real-world scenarios and challenges, allowing you to apply the skills you learn to practical situations, while giving you real hands-on experience.

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Reviewers provide timely and constructive feedback on your project submissions, highlighting areas of improvement and offering practical tips to enhance your work.

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Supervised Learning


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4 Months

Average time to complete a Nanodegree program

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Discount applies to the first 4 months of membership, after which plans are converted to month-to-month.