Exploratory Data Analysis for Machine Learning W7101G

This course targets aspiring data scientists interested in acquiring hands-on experience with Machine Learning and Artificial Intelligence in a business setting.

By the end of this course you should be able to: - Retrieve data from multiple data sources: SQL, NoSQL databases, APIs, Cloud. Describe and use common feature selection and feature engineering techniques.

- Handle categorical and ordinal features, as well as missing values.

- Use a variety of techniques for detecting and dealing with outliers.

- Articulate why feature scaling is important and use a variety of scaling techniques.

To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics.

1. A Brief History of Modern AI and its Applications

2. Retrieving Data, Exploratory Data Analysis, and Feature Engineering

3. Inferential Statistics and Hypothesis Testing