One of the most common tasks performed by data scientists and data analysts is 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 Practical Machine Learning course by Coursera 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.
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
Week 1: Prediction, Errors, and Cross-Validation
This week will cover prediction, the relative importance of steps, errors, and cross-validation.
Week 2: The Caret Package
This week will introduce the caret package, tools for creating features, and preprocessing.
Week 3: Predicting with trees, Random Forests, & Model-Based Predictions
This week we introduce a number of machine learning algorithms you can use to complete your course project.
Week 4: Regularized Regression and Combining Predictors
This week, we will cover regularized regression and combining predictors.
- Jeff Leek
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