An Introduction to Scikit-Learn
Your one stop shop for getting familiar with Scikit-Learn, one of the most important modelling packages in Python.
Description
This course will cover the theory behind many key concepts in the model building workflow. We will look at how to preprocess data and then how to use sklearn to run a series of models, including Regressions, Suport Vector Machines, Neural Networks and Hierarchical Clustering methods. We will also discuss how to evaluate models for their performance and improve them through Cross Validation and Hyperparameter Tuning.
What You Will Learn!
- This course is a one stop shop for an introduction to sklearn, the most commonly used Python package for statistical modelling
- This course will cover all aspects of the modelling workflow.
- We will look at Preprocessing, running Regressions, Classifications, Neural Networks and Clustering algorithms
- We will also cover Evaluation methodology for building highly successful models
Who Should Attend!
- Beginner Python developers with an interest in running Python statistical models.