• Length:
    6 Weeks
  • Effort:
    4–6 hours per week
  • Price:

    FREE
    Add a Verified Certificate for $99 USD

  • Institution
  • Subject:
  • Level:
    Introductory
  • Language:
    English
  • Video Transcript:
    English

Prerequisites

About this course

One of the principal responsibilities of a data scientist is to make reliable predictions based on data. When the amount of data available is enormous, it helps if some of the analysis can be automated. Machine learning is a way of identifying patterns in data and using them to automatically make predictions or decisions. In this data science course, you will learn basic concepts and elements of machine learning.

The two main methods of machine learning you will focus on are regression and classification. Regression is used when you seek to predict a numerical quantity. Classification is used when you try to predict a category (e.g., given information about a financial transaction, predict whether it is fraudulent or legitimate).

For regression, you will learn how to measure the correlation between two variables and compute a best-fit line for making predictions when the underlying relationship is linear. The course will also teach you how to quantify the uncertainty in your prediction using the bootstrap method. These techniques will be motivated by a wide range of examples.

For classification, you will learn the k-nearest neighbor classification algorithm, learn how to measure the effectiveness of your classifier, and apply it to real-world tasks including medical diagnoses and predicting genres of movies.

The course will highlight the assumptions underlying the techniques, and will provide ways to assess whether those assumptions are good. It will also point out pitfalls that lead to overly optimistic or inaccurate predictions.

What you'll learn

  • Fundamental concepts of machine learning
  • Linear regression, correlation, and the phenomenon of regression to the mean
  • Classification using the k-nearest neighbors algorithm
  • How to compare and evaluate the accuracy of machine learning models
  • Basic probability and Bayes’ theorem

Meet your instructors

Ani Adhikari
Teaching Professor of Statistics
UC Berkeley
John DeNero
Giancarlo Teaching Fellow in the EECS Department
UC Berkeley
David Wagner
Professor of Computer Science
UC Berkeley

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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.