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
Instructor: Di Wu
1,648 already enrolled
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Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
38 hours to complete
3 weeks at 12 hours a week
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
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Assessments
7 assignments
Taught in English
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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 assignment1 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 assignment1 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 assignment1 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 assignments
This week you will learn how to evaluate the performance of classifiers using various metrics and visualization techniques.
What's included
1 video1 assignment
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 Alberta
National Taiwan University
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