APPLIED MACHINE LEARNING

This module usesvideo lessons and 12 exercisesto practice exporting and filtering through data usingJupyter Notebook. We will also practice manipulating information byreplacing and combining, identifying outliers, and display the data with graphs.

This module uses4 video lessons深入研究回归算法，在真实的关系，过度拟合和正则化的情况下打击. We will also discuss non-linear relationships and how to model them usingdecision trees. We then discuss using various合奏方法.

This module usesvideo lessons and 11 exercisesto go over how to split data into training and testing sets,construct model pipelines, perform hyperparameter tuning, and cross-validatealternative models to find the top performer. Additionally, we will go over how toevaluate models and visualize predictions.

This module contains3 video lessonsto demonstrate how some learning algorithms are used to solve classification problems. By the end of this module, you will be familiar with Characteristics ofBinary Classification Problems, Regularized Logistic Regression Models, and Decision Tree Ensemble Classification Models.

This module uses videolessons and 9 exercisesto walk through a business case study. We will perform moreadvanced data exploration and visualization and engineer featuresbased on conditional relationships between existing features.

This module usesvideo lessons and 8 exercisesto continue the business case study from the previous module. We will go over how to usestratified random sampling, the confusion matrix and its advantages over R^2, and go into detail overAUROC. After this module, you would have built a machine learning classifier from start to finish.