Introduction to Machine Learning
Course prerequisites: Programming in Python for Data Science
In this module, we will introduce why and how we split our data as well as how cross-validation works on training data. We will also explain two important concepts in machine learning: the fundamental tradeoff and the golden rule.
In this model, we will concentrate on the steps that need to be taken before building a model. Preparation through imputation and scaling is an important step of model building and can be done using tools such as pipelines. Next, we will explore automated hyperparameter optimization.
This module will teach you different encoding methods for categorical variables (ordinal and one-hot encoding) and appropriately set them up. We will also introduce ColumnTransformer and CountVectorizer from the sklearn library and show you how to implement them.