Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.

Dive into machine learning with Python. Learn to build models, preprocess data, and evaluate results using Python libraries like Scikit-learn, pandas, and numpy, to unlock powerful insights for a variety of business and data science applications.

Master machine learning with R, a popular language for data analysis and statistical modeling. Learn how to apply machine learning algorithms, preprocess data, and evaluate models using R’s powerful libraries and tools for data science.

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

Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.

Teach computers how to make decisions and play games with the Minimax Algorithm!

Level up your machine learning skills by using unsupervised learning to find patterns hidden in data.

Get started with machine learning and learn how to build, implement, and evaluate linear regression models.

Sharpen your machine learning skills by learning how to prepare, implement, and assess the K-Nearest Neighbors algorithm.

Predict the probability that a datapoint belongs to a given class with Logistic Regression.

Level up your machine learning skills by learning how to build perceptrons: the foundations of neural networks.

Learn how to build decision trees and then build those trees into random forests.