- Random Forest Algorithm
- Feature Engineering
- Data Processing
- R Programming
- Regression Analysis
- Applied Machine Learning
- Classification And Regression Tree (CART)
- Machine Learning Algorithms
- Predictive Modeling
- Predictive Analytics
- Supervised Learning
- Machine Learning
Practical Machine Learning
Completed by Brandon Wirakesuma
March 14, 2017
8 hours (approximately)
Brandon Wirakesuma's account is verified. Coursera certifies their successful completion of Practical Machine Learning
What you will learn
Use the basic components of building and applying prediction functions
Understand concepts such as training and tests sets, overfitting, and error rates
Describe machine learning methods such as regression or classification trees
Explain the complete process of building prediction functions
Skills you will gain
