In a world where data is abundant, leveraging machines to learn valuable patterns from structured data can be extremely powerful. In this course, we will explore the basics of machine learning, discussing concepts like regression, classification, model evaluation metrics, overfitting, variance versus bias, linear regression, ensemble methods, model selection, and hyperparameter optimization.
You'll come away with a strong understanding of the core concepts in machine learning and the ability to efficiently train and benchmark accurate predictive models.
Students gain hands-on practice with powerful packages like scikit-learn, building complex ETL pipelines to handle data in a variety of formats and techniques, developing models with tools like feature unions and pipelines that allow them to reuse existing models and reduce duplicate work, and practicing tricks like parallelization to speed up prototyping and development.
Mini Project: Working with a real data sets students will take restaurant reviews and, based on various characteristics, build predictive models to predict the restaurant’s score.