What you will learn
- How machine learning is different than descriptive statistics
- Create and evaluate data clusters
- Learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting)
- Explain different approaches for creating predictive models
- Advanced techniques, such as building ensembles, and practical limitations of predictive models
- Build features that meet analysis needs
- Basic machine learning concepts, tasks, and workflow using an example classification problem based on the K-nearest neighbors method, and implemented using the scikit-learn library
- How to control model complexity by applying techniques like regularization to avoid overfitting
- Linear regression (least-squares, ridge, lasso, and polynomial regression), logistic regression, support vector machines, the use of cross-validation for model evaluation, and decision trees.
- Learn about the critical problem of data leakage in machine learning and how to detect and avoid it
This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.
How to Enroll Applied Machine Learning in Python course?
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