Offered By: IBMSkillsNetwork
Efficient models: reduce dimensionality with LDA in Python
Understand and implement Linear Discriminant Analysis (LDA), one of the best ML methods for dimensionality reduction in classification tasks. Dimensionality reduction is a fundamental machine learning technique that is frequently used to improve the performance of prediction models, interpretability, and data visualization. This easy-to-follow, hands-on project walks you through understanding LDA, when it's most useful, and how to implement this dimensionality reduction technique using Python.
Continue readingGuided Project
Machine Learning
At a Glance
Understand and implement Linear Discriminant Analysis (LDA), one of the best ML methods for dimensionality reduction in classification tasks. Dimensionality reduction is a fundamental machine learning technique that is frequently used to improve the performance of prediction models, interpretability, and data visualization. This easy-to-follow, hands-on project walks you through understanding LDA, when it's most useful, and how to implement this dimensionality reduction technique using Python.
A Look at the Project Ahead
- Learn how LDA works
- Plot the LDA decision boundary for a binary classification problem
- Use LDA for classification
- Use LDA for dimensionality reduction
- Learn how to implement LDA using Python
What You'll Need
While having a basic grasp of statistics, data science, and/or machine learning is helpful for following along, it's not strictly required. The project is designed to be as accessible as possible to a general audience, with explanations primarily delivered in a graphical and intuitive manner. Whether you're a beginner just starting out, or a seasoned professional looking for a refresher on LDA, this hands-on project is for you!