- Supervised Learning
- Predictive Modeling
- Data Processing
- Predictive Analytics
- Regression Analysis
- Feature Engineering
- Random Forest Algorithm
- Applied Machine Learning
- Classification And Regression Tree (CART)
- Machine Learning Algorithms
- Machine Learning
- Data Collection
Practical Machine Learning
Completed by Michael Richard Meitzner
June 27, 2018
8 hours (approximately)
Michael Richard Meitzner'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
