Applied Machine Learning
Participants will learn to solve practical machine learning problems using a hands-on approach in application areas such as e-commerce, business intelligence, and bioinformatics. Upon completion of the course, participants will be able to clean data, apply machine learning techniques to solve practical problems, and analyze data in supervised scenarios with an end-to-end approach.
Participants are expected to have basic fluency in python (at least one course) with an ability to apply common libraries (matplotlib, numpy, pandas, scikit-learn) to simple data problems. Knowledge of basic statistics and linear algebra are helpful but not required. Students should be familiar with the topics covered in the Data Institute’s Introduction to Machine Learning course.
The learning outcomes of this certificate course are:
- Apply Random Forests, Gradient boosting machines, and regularized linear models to nontrivial regression and classification problems
- Properly clean and normalize data sets
- Prepare non-numerical features for use in machine learning models
- Identify and rectify models that are overfit