Introduction to Data Mining #

Chapter Flow #

The flow of topics that we cover:

graph TB; A(What is Data Mining) --> B(Way of Working in AIML) B --> C(Data Pipelines) C --> D{Hands-on} D -->|Use Case| E(Uber ML Architecture) D -->|Your Development Environment| F(Colaboratory)

After reading this chapter you should feel familiar with the following ideas and concepts:

1. The wider scope of AI and differences between data mining / machine learning and AI.
2. The data science ecosystem.
3. The functional decomposition of the four pipelines needed to power a data intensive business / application.
4. The complexity behind a real world data pipeline and its tradeoffs (CAP theorem)
5. The Python language basic APIs in data science projects and a tutorial path for you to dig deeper.