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.
Overview
Syllabus
- 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!
- Predictive Modeling for Agriculture
- Linear Classifiers in Python
- In this course you will learn the details of linear classifiers like logistic regression and SVM.
- 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.
- Predicting Movie Rental Durations
- 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.
- Hyperparameter Tuning in Python
- Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.
- 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.
Taught by
Sergey Fogelson, Mike Gelbart, Elie Kawerk, Alex Scriven, Román de las Heras, and George Boorman
