- Supervised Learning
- Random Forest Algorithm
- Applied Machine Learning
- Classification Algorithms
- Feature Engineering
- Predictive Modeling
- Data Preprocessing
- Statistical Machine Learning
- Machine Learning
- Data Wrangling
- Predictive Analytics
- Model Evaluation
Practical Machine Learning
Completed by Pierre LeBlanc
March 10, 2020
8 hours (approximately)
Pierre LeBlanc'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
