Data Preparation & Cleaning for ML

Simply put, data preparation and cleaning are implementations of the principle of "garbage in, garbage out." Identifying and correcting errors, removing damaged and duplicate records, filling in missing values, and handling outliers are all essential steps in preparation. This process can be labor-intensive, but it is quality data that determines a project's success. Even the most advanced machine learning algorithms cannot be trained on unstructured or "dirty" data. To ensure you feel confident in your ML projects, this mini-course will cover everything you need to know about data preparation. - We'll start with an 8-key-step checklist to keep in mind when launching any project. - We'll delve into theory, including missing values, outliers, feature selection, and more. - We'll move on to practice, where for each segment you'll complete tasks in Python, working with real data.