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Data Science: Machine Learning

Provided by Harvard University (HarvardX)
Introductory
See prerequisites
2–4 hours
per week, for 8 weeks
Free

$49 USD for a certificate of completion

Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.

Before you start

This course is part of our Professional Certificate Program in Data Science and we recommend the preceding courses in the series as prerequisites.

Choose your pace

Self-Paced courses contain assignments without due dates. You can progress at your own speed.

Steady Learners
80% complete in less than 20 weeks
Accelerated Learners
50% complete in less than 9 weeks
Course opens: Jul 16, 2019
Course ends: Jan 3, 2020

What you will learn

  • The basics of machine learning
  • How to perform cross-validation to avoid overtraining
  • Several popular machine learning algorithms
  • How to build a recommendation system
  • What is regularization and why it is useful?

Overview

Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors.

In this course, part of our Professional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.

You will learn about training data, and how to use a set of data to discover potentially predictive relationships. As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning.

Meet your instructors

Rafael Irizarry
Professor of Biostatistics
Harvard University

Frequently asked questions

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Who can take this course?

Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. EdX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

View Courses
This course is part of:

Earn a Professional Certificate in 2-4 months if courses are taken one at a time.

View the program
  1. 8–16 hours of effort

    Build a foundation in R and learn how to wrangle, analyze, and visualize data.

  2. 8–16 hours of effort

    Learn basic data visualization principles and how to apply them using ggplot2.

  3. 8–16 hours of effort

    Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008.

  4. 8–16 hours of effort

    Learn inference and modeling, two of the most widely used statistical tools in data analysis.

  5. 8–16 hours of effort

    Keep your projects organized and produce reproducible reports using GitHub, git, Unix/Linux, and RStudio.

  6. 8–16 hours of effort

    Learn to process and convert raw data into formats needed for analysis.

  7. 8–16 hours of effort

    Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science.

  8. Data Science: Machine Learning
  9. 30–40 hours of effort

    Show what you’ve learned from the Professional Certificate Program in Data Science.

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