Classification Analysis
Instructor: Di Wu
Intermediate Level • 4 weeks to complete at 10 hours a week • Flexible Schedule
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
Bayesian Statistics
Data Science
Classification And Regression Tree (CART)
Machine Learning
Supervised Learning
Probability & Statistics
Machine Learning Algorithms
Feature Engineering
Predictive Modeling
Data Mining
Data analysis
Shareable Certificate
Earn a shareable certificate to add to your LinkedIn profile
Outcomes
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
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.
This week you will explore the Decision Tree algorithm, learning its structure, construction, and applications in classification problems.
This week focuses on the Support Vector Machine (SVM) algorithm, where you will grasp its principles and how it is used for classification.
This week will delve into two essential classifiers: Naive Bayes and Logistic Regression. You will gain insights into their assumptions, strengths, and applications.
This week you will learn how to evaluate the performance of classifiers using various metrics and visualization techniques.
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.