Learn how to explore, clean, and prepare data for machine learning model training in this 39-minute video tutorial. Dive into various data types, documentation practices, and notebook setup before examining the dataset. Master techniques for handling missing values, identifying outliers, and addressing issues with categorical variables. Prepare the target feature and finalize data preparation for model training. Access the provided code and flight delays dataset to follow along. Explore essential Python libraries and tools for AI development, including links to Python, Miniconda, and VS Code. Gain practical skills in data preprocessing, a crucial step in the machine learning pipeline.
Overview
Syllabus
- Intro
- Types of data
- Data documentation
- Settings up the notebook
- First look at the data
- Missing values
- Outliers
- Issues with categorical values
- Preparing the target feature
- Final prep for model training
Taught by
AssemblyAI
