commit-live-students / day01-introduction-to-ml

Pre-Read

1. Representational State Application Programming Interface (REST)
   • History of APIs
   • REST API concepts and examples
2. Document Object Model (DOM)
   • What is the Document Object Model
   • Coding 101: An introduction to DOM
3. Hyper Text Markup Language (HTML)
   • Introduction to HTML
   • Watch this video for a fun, short understanding
4. XML
   • Basics of XML
   • Bonus Reading: XPath Introduction
5. Javascript Object Notation (JSON) - JSON in a Nutshell
6. Yet Another Markup Language (YAML) - YAML Wiki

Learning Objectives

1. Understand what machine learning is, and how it fits in with Data Science
2. Understand various types of machine learning, particularly supervised and unsupervised machine learning and their applications
3. Understand the machine learning process and the various stages in it from a 30-thousand feet perspective
4. Understand the role data plays in machine learning, and the various places it comes from
5. Understand the various places data comes from, and what it may mean for you as a data scientist inside an organisation
6. Understand three key places you will be interacting with data - sql databases, nosql stores and the web (via apis)

Hands-on Skills

1. Learn how to set up Twitter and GitHub api access with tokens
2. Learn how to build a simple GitHub recommender system using the GitHub api
3. Learn how to parse nosql data (YAML, JSON) and transform it to rectangular dataset to answer questions

Slides

@gslides

Notebooks

Code demonstration

Assignments

Github Recommender System

Parsing YAML

Post Reads

1. A Visual Introduction to Machine Learning
2. 10 Algorithms every Machine Learning Engineer should know about
3. CRISP-DM and why should you know about it