Introduction to Data Mining

Introduction to Data Mining #

Chapter Flow #

The flow of topics that we cover:

Use Case
Your Development Environment
What is Data Mining
Way of Working in AIML
Data Pipelines
Hands-on
Uber ML Architecture
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.
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