Skip to main content

course

Supervised Learning with scikit-learn

Intermediate

4.3+

Updated 12/2024

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!

Start course for free

Included for FreePremium or Teams

PythonMachine Learning4 hours15 videos49 exercises4,050 XP157,938Statement of Accomplishment

Create Your Free Account

Google LinkedIn Facebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Training 2 or more people?

Try DataCamp for Business

Loved by learners at thousands of companies

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

Congratulations

Supervised Learning with scikit-learn

Course
Complete

Earn Statement of Accomplishment

Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review

Included withPremium or Teams

Don’t just take our word for it

*4.3

from 146 reviews

62%

18%

17%

3%

0%

Highest to Lowest
Lowest to Highest
Most recent
Top reviews

Wook S.

29 days

I can learn a lot from a well-trained instructor. Well-designed hands-on activities are also very helpful.

Batuhan C.

30 days

Good course

Frauke W.

about 1 month

New view and mind-building possibilities

tze L.

about 1 month

Taught the concepts well, for a complete beginner in machine learning.

idriss k.

about 2 months

great

"I can learn a lot from a well-trained instructor. Well-designed hands-on activities are also very helpful."

Wook S.

"Good course"

Batuhan C.

"New view and mind-building possibilities"

Frauke W.

FAQs

Join over 15 million learners and start Supervised Learning with scikit-learn today!

Create Your Free Account

Google LinkedIn Facebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.