Supervised Learning 4: Non-linear Supervised Machine Learning Algorithms
This course is available only as a part of subscription plans.
Training duration: 90 min (Hands-on)
Summarize how each algorithm works
Describe which hyperparameters need to be tuned and what range the values should have
Apply the algorithms in regression and classification
Visualize the predictions of toy datasets
Summarize under what circumstances a certain algorithm is expected to perform well or poorly and why
Andras Zsom, PhD
Andras Zsom, PhD
Lead Data Scientist and Adjunct Lecturer in Data Science | Brown University, Center for Computation and Visualization
Module 1: KNN
Module 2: SVM
Module 3: RF
Module 4: XGBoost
Python coding experience
Familiarity with pandas and numpy
Prior experience with scikit-learn and matplotlib are a plus but not required