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The course. Basics of Practical Machine Learning will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.
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About this Course
One of the most common tasks performed by data scientists and data analysts is prediction and machine learning. This course, Basics of Practical Machine Learning 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.
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 in Basics of Practical Machine Learning
Skills You Will Gain
- Random Forest in relations to Basics of Practical Machine Learning
- Machine Learning (ML) Algorithms
- Machine Learning in Basics of Practical Machine Learning
- R Programming
2 hours to complete
Week 1: Prediction, Errors, and Cross-Validation
This week will cover prediction, the relative importance of steps, errors, and cross-validation.
9 videos (Total 73 min), 4 readings, 1 quiz
2 hours to complete
Week 2: Basics of Practical Machine Learning: The Caret Package
This week will introduce the caret package, tools for creating features and preprocessing.
9 videos (Total 96 min)
1 hour to complete
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.
5 videos (Total 48 min)
3 hours to complete
Week 4: Regularized Regression and Combining Predictors
This week, we will cover regularized regression and combining predictors.
4 videos (Total 33 min), 2 readings, 3 quizzes