- Feature Engineering
- Statistical Machine Learning
- Data Preprocessing
- Predictive Modeling
- Model Evaluation
- R Programming
- Supervised Learning
- Data Wrangling
- Regression Analysis
- Predictive Analytics
- Applied Machine Learning
- Random Forest Algorithm
Practical Machine Learning
Completed by Shelby Bachman
June 22, 2019
8 hours (approximately)
Shelby Bachman'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
