Supervised Learning with scikit-learn Data Camp

Overview

Context

Grow your machine learning skills with scikit-learn and discover how to use this popular Python library to train models using labeled data. 

In this Supervised Learning with scikit-learn course offered by Data Camp, you'll learn how to make powerful predictions, such as whether a customer is will churn from your business, whether an individual has diabetes, and even how to tell classify the genre of a song. 

Using real-world datasets, you'll find out how to build predictive models, tune their parameters, and determine how well they will perform with unseen data.

What you'll learn:

• Assess model generalization using train-test splits, k-fold cross-validation, and hyperparameter tuning with GridSearchCV or RandomizedSearchCV
• Differentiate key evaluation metrics for supervised models, including accuracy, precision, recall, F1, ROC-AUC, R-squared, MSE, and RMSE
• Evaluate model complexity and its impact on overfitting or underfitting by adjusting parameters such as k in KNN and alpha in regularized regression.
• Identify supervised learning problem types and select appropriate scikit-learn algorithms for classification and regression
• Recognize essential preprocessing techniques—dummy encoding, imputation, scaling, and pipeline construction—required for scikit-learn workflows