The lessons of this course help you mastering the use of decision trees and random forests for your data analysis projects. The course focuses on decision tree classifiers and random forest classifiers because most of the successful machine learning applications appear to be classification problems. The lessons explain:
Decision trees for classification problems.
Elements of growing decision trees.
The sklearn parameters to define decision tree classifiers.
Prediction with decision trees using Scikit-learn (fitting, pruning/tuning, investigating).
The sklearn parameters to define random forest classifiers.
Prediction with random forests using Scikit-learn (fitting, tuning, investigating).
The ideas behind random forests for prediction.
Characteristics of fitted decision trees and random forests.
Importance of data and understanding prediction performance.
How you can carry out a prediction project using decision trees and random forests.
Focusing on classification problems, the course uses the DecisionTreeClassifier and RandomForestClassifier methods of Python’s Scikit-learn library. It prepares you for using decision trees and random forests to make predictions and understanding the predictive structure of data sets.
This is what is inside the lessons:
This course is for people who want to use decision trees or random forests for prediction with Scikit-learn. This requires practical experience and the course facilitates you with Jupyter notebooks to review and practice the lessons’ topics.
Each lesson is a short video to watch. Most of the lessons explain something about decision trees or random forests with an example in a Jupyter notebook. The course materials include more than 50 Jupyter notebooks and the corresponding Python code. You can download the notebooks of the lessons for review. You can also use the notebooks to try other definitions of decision trees and random forests or other data for further practice.
