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In this course, you will learn :
- How to use Python to perform supervised learning, which is a necessary component of machine learning
- You'll learn how to create predictive models, fine-tune their parameters, and assess how well they'll perform with unknown data—all while working with real-world datasets.
- You'll be working with scikit-learn, one of Python's most popular and user-friendly machine learning libraries.
- Learn about some of the other metrics available in scikit-learn that will allow you to evaluate the performance of your model in a more nuanced way.
- Learn about fundamental regression concepts and how to apply them to predict life expectancy in a given country using Gapminder data.
Syllabus :
- Classification
- Regression
- Fine-tuning your model
- Preprocessing and pipelines
