- Data Processing
- Regression Analysis
- R Programming
- Machine Learning Algorithms
- Applied Machine Learning
- Classification And Regression Tree (CART)
- Random Forest Algorithm
- Predictive Analytics
- Data Collection
- Predictive Modeling
- Feature Engineering
- Supervised Learning
Practical Machine Learning
Completed by Paul Johannes Schweigl
May 31, 2020
8 hours (approximately)
Paul Johannes Schweigl'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
