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Supervised Machine Learning in Python

Updated 03/2026
Master the most popular supervised machine learning techniques to begin making predictions with labeled data.
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PythonMachine Learning25 hr5,790

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Track Description

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.

Prerequisites

There are no prerequisites for this track

  • Course

    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!

  • Project

    bonus

    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.

  • Course

    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.

  • Course

    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.

Supervised Machine Learning in Python
6 Courses
Track
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FAQs

Is this Track suitable for beginners?

Yes, this Track is suitable for beginners who are new to machine learning or want to specialize in supervised machine learning.

What is the programming language of this Track?

The programming language used in this Track is Python.

Which jobs will benefit from this Track?

This track will benefit individuals who are interested in machine learning and data analysis, and those pursuing careers in fields such as data science, artificial intelligence, and data engineering.

How will this Track prepare me for my career?

This Track will provide you with the essential knowledge and skills in supervised machine learning, which are in high demand in various industries. It will help you build a strong foundation for a successful career in data science or related fields.

How long does it take to complete this Track?

On average, it takes approximately 25 hours to complete this Track. However, users can work through the self-paced exercises and courses at their own pace.

What's the difference between a skill track and a career track?

A skill track is a collection of courses designed to help individuals develop specific skills, while a career track provides a more comprehensive learning experience and prepares individuals for specific job roles or career paths.

What is the difference between supervised and unsupervised learning?

Supervised learning uses labeled data to train models and make predictions, while unsupervised learning leverages unlabeled data to find patterns and insights.

Which libraries will be used in this Track?

The popular scikit-learn library will be used in this Track, along with state-of-the-art algorithms like XGBoost.

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