Build practical machine learning models using Scikit-learn, the popular Python library. This course covers fundamental concepts, data handling, model training, evaluation, and building streamlined workflows with pipelines. Gain hands-on experience applying common algorithms for classification and regression tasks.
Prerequisites Basic Python/Pandas
Level:
Scikit-learn Core API
Understand and utilize the main components of the Scikit-learn library, including Estimators, Predictors, and Transformers.
Data Preparation
Apply common data preprocessing techniques like scaling, encoding categorical features, and handling missing values using Scikit-learn.
Supervised Learning Models
Implement and evaluate common supervised learning algorithms for regression and classification tasks.
Model Evaluation
Select appropriate metrics and use cross-validation techniques to assess model performance reliably.
Pipeline Construction
Build and manage machine learning workflows using Scikit-learn Pipelines to combine preprocessing and modeling steps.