The "Classification Analysis" course provides you with a comprehensive understanding of one of the fundamental supervised learning methods, classification. You will explore various classifiers, including KNN, decision tree, support vector machine, naive bayes, and logistic regression, and learn how to evaluate their performance. Through tutorials and engaging case studies, you will gain hands-on experience and practice in applying classification techniques to real-world data analysis tasks.
Classification Analysis
Classification Analysis
This course is part of Data Analysis with Python Specialization
Taught in English
Instructor: Di Wu
Included with 
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.
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
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
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.
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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
Offered by
Recommended if you're interested in Data Analysis
University of Colorado Boulder
University of Colorado Boulder
University of Colorado Boulder
University of Colorado Boulder
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