In this course, We will learn how to use scikit-learn to build predictive models from data. We will explore the basic concepts of machine learning and see how to use scikit-learn to solve supervised and unsupervised learning problems. We will also learn how to evaluate models, tune parameters, and avoid common pitfalls. We will work through examples of machine learning problems using real-world datasets.
15 Labs
Linear ModelsStart
Linear and Quadratic Discriminant AnalysisStart
Statistical LearningStart
Kernel Ridge RegressionStart
Supervised LearningStart
Model Selection: Choosing Estimators and Their ParametersStart
Support Vector MachinesStart
Stochastic Gradient DescentStart
Unsupervised Learning: Seeking Representations of the DataStart
Stochastic Gradient DescentStart
Working With Text DataStart
Gaussian ProcessesStart
Cross DecompositionStart
Naive Bayes ExampleStart
Decision TreesStart
