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.
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By the end of this course, you will be able to:
- Understand the concept and significance of classification as a supervised learning method.
- Identify and describe different classifiers, such as KNN, decision tree, support vector machine, naive bayes, and logistic regression.
- Apply each classifier to perform binary and multiclass classification tasks on diverse datasets.
- Evaluate the performance of classifiers using appropriate metrics, including accuracy, precision, recall, F1 score, and ROC curves.
- Select and fine-tune classifiers based on dataset characteristics and learning requirements.
Gain practical experience in solving classification problems through guided tutorials and case studies.
This course is part of the Data Analysis with Python Specialization.
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.
Syllabus
Introduction to Classification
Module 1
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.
Decision Tree Classification
Module 2
This week you will explore the Decision Tree algorithm, learning its structure, construction, and applications in classification problems.
Support Vector Machine Classification
Module 3
This week focuses on the Support Vector Machine (SVM) algorithm, where you will grasp its principles and how it is used for classification.
Naïve Bayes and Logistic Regression
Module 4
This week will delve into two essential classifiers: Naive Bayes and Logistic Regression. You will gain insights into their assumptions, strengths, and applications.
Classification Evaluation
Module 5
This week you will learn how to evaluate the performance of classifiers using various metrics and visualization techniques.
Case Study
Module 6
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.
