title: Machine Learning: An Introductory Handbook Using R
author: Dr. Karen J Mazidi
description: Dr. Karen Mazidi is a computer science researcher and educator with over 30 years experience in the field. Her main research interests are Natural Language Processing and Machine Learning.
zlib_text: 1.	 1.	The	Machine	Learning	Landscape
  a.	 What	Is	Machine	Learning?
  b.	 Why	Use	Machine	Learning?
  c.	 Types	of	Machine	Learning	Systems
  i.	 Supervised/Unsupervised	Learning
  ii.	 Batch	and	Online	Learning
  iii.	 Instance-Based	Versus	Model-Based
  Learning
  d.	 Main	Challenges	of	Machine	Learning
  i.	 Insufficient	Quantity	of	Training	Data
  ii.	 Nonrepresentative	Training	Data
  iii.	 Poor-Quality	Data
  iv.	 Irrelevant	Features
  v.	 Overfitting	the	Training	Data
  vi.	 Underfitting	the	Training	Data
  vii.	 Stepping	Back
  e.	 Testing	and	Validating
  f.	 Exercises
  2.	 2.	End-to-End	Machine	Learning	Project
  a.	 Working	with	Real	Data
  b.	 Look	at	the	Big	Picture
  i.	 Frame	the	Problem
  ii.	 Select	a	Performance	Measure
  iii.	 Check	the	Assumptions
  c.	 Get	the	Data
  
  i.	 Create	the	Workspace
  ii.	 Download	the	Data
  iii.	 Take	a	Quick	Look	at	the	Data	Structure
  iv.	 Create	a	Test	Set
  d.	 Discover	and	Visualize	the	Data	to	Gain	Insights
  i.	 Visualizing	Geographical	Data
  ii.	 Looking	for	Correlations
  iii.	 Experim