Artificial Intelligence with Scikit-Learn
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Description
This course focuses on the application of scikit-learn, a popular open-source Python library, for both supervised and unsupervised machine learning. It covers practical techniques such as linear and logistic regression, decision trees, random forest models, K-means clustering, and principal component analysis (PCA). Additionally, it teaches how to create scikit-learn pipelines for cleaner, bug-resilient code. By the end of the course, you'll be able to understand the strengths and weaknesses of each scikit-learn algorithm and build more efficient machine learning models.
Learning Objectives
- Understanding the benefits of using scikit-learn.
- Distinguishing between supervised and unsupervised learning.
- Learning linear and logistic regression techniques.
- Understanding decision trees and random forests.
- Learning K-means clustering technique.
- Understanding principal component analysis (PCA).