Introduction to Data Science and Supervised Machine Learning

Learn about the basics of data science and machine learning. Walk through the CRISP-DM process and how you can apply it to many data science problems.

Explore supervised machine learning algorithms: regression, classification, linear models, decision trees, random forests, and neural networks, with interactive exercises in scikit-learn.

Learn why default accuracy metrics can be misleading with real-world datasets, and the alternative metrics you can utilize to communicate the benefits and limitations of your models.

To apply AI ethically and transparently, you need to understand how your models make decisions and whether their impacts are fair. Apply feature importances, SHAP values, and the Aequitas framework

Create a GitHub repository and Medium blog post to communicate your findings