Ensemble ML Algorithms
Explore stochastic gradient descent, decision tree, k-nearest neighbors, and support vector classification algorithms. Learn how to set up pipelines, customize scoring metrics, and tune hyperparameters with grid search. Understand how majority voting rule works through ensembling with the VotingClassifier. And compare the results with the RandomForestClassifier.
JetBrains Academy
About
Data scientists often train and optimize different machine learning models and then choose the one with the best performance. But did you know that combining these models can make them even better? This project will teach you how to train and optimize multiple models and then combine them to get better results. You will discover how ensemble learning can outperform using a single model by working on a multi-class classification task about music and emotions.
Graduate project
This project covers the core topics of the Data Scientist course, making it sufficiently challenging to be a proud addition to your portfolio.
At least one graduate project is required to complete the course.