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Course

Supervised Learning with scikit-learn

Intermediate

4.8+

Updated 04/2025

Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!

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PythonMachine Learning4 hours15 videos49 Exercises4,050 XP187,275Statement of Accomplishment

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Course Description

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 course, 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.

Prerequisites

Introduction to Statistics in Python

1

Classification

Machine learning with scikit-learn

Binary classification

The supervised learning workflow

The classification challenge

k-Nearest Neighbors: Fit

k-Nearest Neighbors: Predict

Measuring model performance

Train/test split + computing accuracy

Overfitting and underfitting

Visualizing model complexity

2

Regression

Introduction to regression

Creating features

Building a linear regression model

Visualizing a linear regression model

The basics of linear regression

Fit and predict for regression

Regression performance

Cross-validation

Cross-validation for R-squared

Analyzing cross-validation metrics

Regularized regression

Regularized regression: Ridge

Lasso regression for feature importance

3

Fine-Tuning Your Model

How good is your model?

Deciding on a primary metric

Assessing a diabetes prediction classifier

Logistic regression and the ROC curve

Building a logistic regression model

The ROC curve

Hyperparameter tuning

Hyperparameter tuning with GridSearchCV

Hyperparameter tuning with RandomizedSearchCV

4

Preprocessing and Pipelines

Preprocessing data

Creating dummy variables

Regression with categorical features

Handling missing data

Dropping missing data

Pipeline for song genre prediction: I

Pipeline for song genre prediction: II

Centering and scaling

Centering and scaling for regression

Centering and scaling for classification

Evaluating multiple models

Visualizing regression model performance

Predicting on the test set

Visualizing classification model performance

Pipeline for predicting song popularity

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Supervised Learning with scikit-learn

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