Practical Machine Learning
Updated on
Course overview
- Provider
- Coursera
- Course type
- Free online course
- Level
- Mixed
- Deadline
- Flexible
- Duration
- 9 hours
- Certificate
- Paid Certificate Available
- Course author
- Jeff Leek, PhD
-
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
Description
One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.
