About this course: Are you interested in predicting future outcomes using your data? This course helps you do just that! Machine learning is the process of developing, testing, and applying predictive algorithms to achieve this goal. Make sure to familiarize yourself with course 3 of this specialization before diving into these machine learning concepts. Building on Course 3, which introduces students to integral supervised machine learning concepts, this course will provide an overview of many additional concepts, techniques, and algorithms in machine learning, from basic classification to decision trees and clustering. By completing this course, you will learn how to apply, test, and interpret machine learning algorithms as alternative methods for addressing your research questions.

Created by: Wesleyan University

Taught by: Jen Rose, Research Professor
Psychology
Taught by: Lisa Dierker, Professor
Psychology

Basic Info	Course 4 of 5 in the Data Analysis and Interpretation Specialization.
Language	English
How To Pass	Pass all graded assignments to complete the course.
User Ratings	4.1 stars Average User Rating 4.1See what learners said

Syllabus

WEEK 1

Decision Trees

In this session, you will learn about decision trees, a type of data mining algorithm that can select from among a large number of variables those and their interactions that are most important in predicting the target or response variable to be explained. Dec...

7 videos, 15 readings

Graded: Running a Classification Tree

WEEK 2

Random Forests

In this session, you will learn about random forests, a type of data mining algorithm that can select from among a large number of variables those that are most important in determining the target or response variable to be explained. Unlike decision trees, th...

4 videos, 4 readings

Graded: Running a Random Forest

WEEK 3

Lasso Regression

Lasso regression analysis is a shrinkage and variable selection method for linear regression models. The goal of lasso regression is to obtain the subset of predictors that minimizes prediction error for a quantitative response variable. The lasso does this by...

5 videos, 3 readings

Graded: Running a Lasso Regression Analysis

WEEK 4

K-Means Cluster Analysis

Cluster analysis is an unsupervised machine learning method that partitions the observations in a data set into a smaller set of clusters where each observation belongs to only one cluster. The goal of cluster analysis is to group, or cluster, observations int...

6 videos, 3 readings

Graded: Running a k-means Cluster Analysis

FAQs

How It Works

Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

Help from Your Peers

Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

Certificates

Earn official recognition for your work, and share your success with friends, colleagues, and employers.

Creators

Wesleyan University

At Wesleyan, distinguished scholar-teachers work closely with students, taking advantage of fluidity among disciplines to explore the world with a variety of tools. The university seeks to build a diverse, energetic community of students, faculty, and staff who think critically and creatively and who value independence of mind and generosity of spirit.

Pricing

	Audit	Purchase Course
Access to course materials	Available	Available
Access to graded materials	Not available	Available
Receive a final grade	Not available	Available
Earn a shareable Course Certificate	Not available	Available

Ratings and Reviews

Rated 4.1 out of 5 of 137 ratings

Excellet! I highly recommend!

Very good. I enjoyed doing it and learned a lot.

I would have liked that it had included r as one of the softwares.

easy to capture the concept

Excellent introductory course on machine learning focusing on simple linear and multiple regression, lasso regression and k-means clustering. A background in Python programming is useful but not required as the instructors discuss the techniques with annotated code examples.

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Machine Learning for Data Analysis

Machine Learning for Data Analysis

Machine Learning for Data Analysis