Trees, SVM and Unsupervised Learning
Completed by Shunsuke Suzuki
January 3, 2026
12 hours (approximately)
Shunsuke Suzuki's account is verified. Coursera certifies their successful completion of Trees, SVM and Unsupervised Learning
What you will learn
Describe the advantages and disadvantages of trees, and how and when to use them.
Apply SVMs for binary classification or K > 2 classes.
Analyze the strengths and weaknesses of neural networks compared to other machine learning algorithms, such as SVMs.
Skills you will gain
- Category: Machine Learning Algorithms
- Category: Supervised Learning
- Category: Model Evaluation
- Category: Applied Machine Learning
- Category: Dimensionality Reduction
- Category: Artificial Neural Networks
- Category: Statistics
- Category: Decision Tree Learning
- Category: Statistical Machine Learning
- Category: Unsupervised Learning
- Category: Machine Learning Methods
- Category: Applied Mathematics
