Machine Learning Foundations with Python


Units: 12


Updating description

Learning Outcomes

1.    Be able to produce, comprehend and run Python code for commonly used machine learning methods.
2.    Understand feature engineering concepts, and be able to place them into practice through different types of data.
3.    Understand and apply both supervised and unsupervised learning techniques
4.    Understand the advantages and disadvantages of machine learning algorithms. This involves generalizability, bias-variance trade-off, and interpretability-flexibility tradeoff.
5.    Be able to choose appropriate model/s for a dataset and evaluate the performance and reliability of such model/s.
6.    Be able to apply machine learning to real-world data.
7.    Understand and follow equitable and good data practices.

Prerequisites Description

- Python proficiency: 95-888 Data Focused Python or 90-819 Intermediate Programming with Python.
- A statistics course such as 90-707, 90-711, or 95-796 
- 90-800 Exploratory Data Analysis and Visualization with Python

All three of the above requirements must be met to take this course.