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Supervised Machine Learning in Python

Master the fundamentals of supervised machine learning and discover how to make predictions using labeled data. Join the ML revolution today! If you’re new to machine learning, or want to specialize in supervised machine learning, this is an ideal place to start. You’ll start by learning about and implementing core supervised learning models, such as K-Nearest Neighbors (KNN), Logistic Regression, Linear Regression, Support Vector Machines (SVMs), and tree-based models with the popular scikit-learn library. You’ll also discover how to use state-of-the-art algorithms like XGBoost to efficiently boost modelling performance on tabular datasets. To get the most out of your models, you’ll learn about different hyperparameter tuning techniques and how to decide which technique to use for your use case. You’ll finish the track by bringing your knowledge of these diverse models together to learn about ensemble learning, where different models are combined to improve performance and solve more complex problems. By the time you’re finished, you’ll have mastered the essential supervised machine learning concepts and be able to apply them in Python.

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1
Supervised Learning with scikit-learn

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!

4 hours

George Boorman Headshot

George Boorman

Curriculum Manager, DataCamp

2
Predictive Modeling for Agriculture

Dive into agriculture using supervised machine learning and feature selection to aid farmers in crop cultivation and solve real-world problems.

2 hours

3
Linear Classifiers in Python

In this course you will learn the details of linear classifiers like logistic regression and SVM.

4 hours

Mike Gelbart Headshot

Mike Gelbart

Instructor, the University of British Columbia

4
Machine Learning with Tree-Based Models in Python

In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.

5 hours

Elie Kawerk Headshot

Elie Kawerk

Senior Data Scientist

5
Predicting Movie Rental Durations

Build a regression model for a DVD rental firm to predict rental duration. Evaluate models to recommend the best one.

2 hours

Rohan Chatterjee Headshot

Rohan Chatterjee

Risk Modeler

6
Extreme Gradient Boosting with XGBoost

Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.

4 hours

Sergey Fogelson Headshot

Sergey Fogelson

Head of Data Science, TelevisaUnivision

7
Hyperparameter Tuning in Python

Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.

4 hours

Alex Scriven Headshot

Alex Scriven

Senior Data Scientist @ Atlassian

8
Ensemble Methods in Python

Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.

4 hours

Román de las Heras Headshot

Román de las Heras

Data Scientist at Appodeal

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Instructors

  • George Boorman Headshot
    George BoormanCurriculum Manager, DataCampSee George Boorman's Portfolio
  • Mohammed Abufouda Headshot
    Mohammed AbufoudaSee Mohammed Abufouda's Portfolio
  • Mike Gelbart Headshot
    Mike GelbartInstructor, the University of British ColumbiaSee Mike Gelbart's Portfolio
  • Elie Kawerk Headshot
    Elie KawerkSenior Data ScientistSee Elie Kawerk's Portfolio

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