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Understanding Decision Trees and Random Forests
Created by a Stanford alumni team, this decision trees and random forests tutorial teaches cool machine learning techniques to predict the survival probabilities aboard the Titanic!
Decision Trees are a graphic and intuitive method of predicting the outcome of a given input. They attach a weightage to the input variables and help you clearly detect what really influences your outcome. Building a Decision Tree is a tedious procedure, as they have the tendency to overfit. That's where Random Forests come into the picture. Random Forests use an ensemble of Decision Trees, this reduces the complexities without compromising on the advantages. This decision trees and random forests tutorial enhances your knowledge about the influence of these concepts in Machine Learning.
What will you gain from this course?
- The skill to pin-point the use-cases for decision trees and random forests
- The ability to design and apply the solution to a well known Machine Learning problem - predicting survival probabilities aboard the Titanic
- An understanding of the danger of overfitting, and how random forests help overcome this risk
Prerequisites and Target Audience
Knowledge of undergraduate level Mathematics will make understanding this course easier, however, it is not a prerequisite. If you would like to run the source code, you will require working knowledge of Python. This course is designed for:
- Engineers who are interested in learning Machine Learning and applying it to solve problems
- Big data professionals, analytics professionals, and modelers who desire to learn Machine Learning
- Technical Executives and Investors who are excited about Machine Learning, big data, or natural language processing
- Product Managers who desire to have intellectual conversations with data scientists about Machine Learning