University of Colorado Boulder

Classification Analysis

This course is part of Data Analysis with Python Specialization

Taught in English

Di Wu

Instructor: Di Wu

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

38 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Understand the concept and significance of classification as a supervised learning method.

  • Identify and describe different classifiers, apply each classifier to perform binary and multiclass classification tasks on diverse datasets.

  • Evaluate the performance of classifiers, select and fine-tune classifiers based on dataset characteristics and learning requirements.

Details to know

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Assessments

6 quizzes, 1 assignment

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

38 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the Data Analysis with Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 6 modules in this course

This week provides an overview of classification as a supervised learning method. You will also learn the K-Nearest Neighbors (KNN) algorithm, understanding its principles and applications in classification tasks.

What's included

2 videos5 readings1 quiz1 discussion prompt

This week you will explore the Decision Tree algorithm, learning its structure, construction, and applications in classification problems.

What's included

1 video3 readings1 quiz1 discussion prompt

This week focuses on the Support Vector Machine (SVM) algorithm, where you will grasp its principles and how it is used for classification.

What's included

1 video3 readings1 quiz1 discussion prompt

This week will delve into two essential classifiers: Naive Bayes and Logistic Regression. You will gain insights into their assumptions, strengths, and applications.

What's included

2 videos6 readings2 quizzes

This week you will learn how to evaluate the performance of classifiers using various metrics and visualization techniques.

What's included

1 video1 quiz

In this final week, you will apply the knowledge and techniques learned throughout the course to solve a real-world classification problem through a comprehensive case study.

What's included

2 readings1 assignment1 discussion prompt

Instructor

Di Wu
University of Colorado Boulder
15 Courses29,139 learners

Offered by

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