- Random Forest Algorithm
- Applied Machine Learning
- Decision Tree Learning
- Predictive Modeling
- Unsupervised Learning
- Classification And Regression Tree (CART)
- Supervised Learning
- Machine Learning
- Dimensionality Reduction
- Data Science
- Statistics
- Artificial Neural Networks
Trees, SVM and Unsupervised Learning
Completed by Andres Restrepo Rodriguez
December 1, 2023
12 hours (approximately)
Andres Restrepo Rodriguez'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
