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In this course you will learn:
- Learn the fundamentals of data science and machine learning. Walk through the CRISP-DM process and how it may be applied to a variety of data science problems.
- Interactive activities in scikit-learn allow you to learn about supervised machine learning methods such as regression, classification, linear models, decision trees, random forests, and neural networks.
- Learn why default accuracy measures can be misleading when used with real-world datasets, as well as alternate metrics for communicating your models' benefits and limitations.
- To use AI ethically and transparently, you must understand how your models make decisions and whether their consequences are equitable. Use feature importances, SHAP values, and the Aequitas framework.
- Create a GitHub repository and Medium blog post to explain your findings.
- Complete the CRISP-DM process with your preferred dataset and present your findings in the form of a blog post.
Syllabus:
- The Data Science Process
- Supervised Machine Learning Algorithms
- Machine Learning Model Evaluation
- Model Interpretability and Fairness
- Communicating to Stakeholders
- Project: Data Science Blog Post
