Applied Machine Learning: Algorithms
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Description
This course is the second installment in the Applied Machine Learning series, delving into a variety of algorithms from logistic regression to gradient boosting. It provides a structured approach to choosing the best algorithm for a given problem, considering each algorithm's pros and cons. The course enhances your understanding of what drives each algorithm, their benefits, and drawbacks, equipping you with a significant competitive advantage as a data scientist.
Learning Objectives
- Understanding models vs. algorithms.
- Cleaning continuous and categorical variables.
- Tuning hyperparameters.
- Learning logistic regression basics.
- Fitting a support vector machines model.
- Understanding when to use a multi-layer perceptron model.
- Using the random forest algorithm.
- Fitting a basic boosting model.