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Supervised Machine Learning in Python

Master the most popular supervised machine learning techniques to begin making predictions with labeled data.
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PythonMachine Learning25 hours2,626

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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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