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Supervised Learning with scikit-learn

IntermediateSkill Level
4.8+
7,846 reviews
Updated 12/2025
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 Learning
4 hr
15 videos
49 Exercises
4,050 XP
270K+
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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.The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos. The course glossary can be found on the right in the resources section.To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.

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

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Prerequisites

Introduction to Statistics in Python
1

Classification

In this chapter, you'll be introduced to classification problems and learn how to solve them using supervised learning techniques. You'll learn how to split data into training and test sets, fit a model, make predictions, and evaluate accuracy. You’ll discover the relationship between model complexity and performance, applying what you learn to a churn dataset, where you will classify the churn status of a telecom company's customers.
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2

Regression

In this chapter, you will be introduced to regression, and build models to predict sales values using a dataset on advertising expenditure. You will learn about the mechanics of linear regression and common performance metrics such as R-squared and root mean squared error. You will perform k-fold cross-validation, and apply regularization to regression models to reduce the risk of overfitting.
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3

Fine-Tuning Your Model

Having trained models, now you will learn how to evaluate them. In this chapter, you will be introduced to several metrics along with a visualization technique for analyzing classification model performance using scikit-learn. You will also learn how to optimize classification and regression models through the use of hyperparameter tuning.
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4

Preprocessing and Pipelines

Learn how to impute missing values, convert categorical data to numeric values, scale data, evaluate multiple supervised learning models simultaneously, and build pipelines to streamline your workflow!
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Supervised Learning with scikit-learn
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*4.8
from 7,846 reviews
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  • Stanley
    2 hours ago

  • Mubarak
    4 hours ago

  • Erisa
    5 hours ago

    some parts did not explain very well. i had to use AI to understand it a bit better. some exercise hints are not helpful.

  • MOST
    6 hours ago

  • JUAN RAMON
    7 hours ago

    Buen curso

  • Luis
    14 hours ago

Mubarak

MOST

"Buen curso"

JUAN RAMON

FAQs

Who will benefit from this course?

This course is beneficial for anyone interested in data analysis, machine learning, and related fields. People working in finance, analytics, data science, economics, software engineering, and other related fields would find this course useful.

Will I receive a certificate at the end of the course?

Yes, upon completion of this course you will receive a DataCamp certificate.

What topics does this course cover?

This course covers supervised learning methods, regression, data pre-processing, building pipelines, fine-tuning models, and more. It will also demonstrate how to use the scikit-learn library to solve classification and regression problems.

What is classification?

Classification is a supervised machine learning technique used for predicting discrete values for a given set of inputs.

What is regression?

Regression is a supervised machine learning technique used for predicting continuous values for a given set of inputs.

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