Machine learning for practical decision making [electronic resource] : a multidisciplinary perspective with applications from healthcare, engineering and business analytics / Christo El Morr, Manar Jammal, Hossam Ali-Hassan, Walid El-Hallak.

Intro
Preface
Contents
Chapter 1: Introduction to Machine Learning
1.1 Introduction to Machine Learning
1.2 Origin of Machine Learning
1.3 Growth of Machine Learning
1.4 How Machine Learning Works
1.5 Machine Learning Building Blocks
1.5.1 Data Management and Exploration
1.5.1.1 Data, Information, and Knowledge
1.5.1.2 Big Data
1.5.1.3 OLAP Versus OLTP
1.5.1.4 Databases, Data Warehouses, and Data Marts
1.5.1.5 Multidimensional Analysis Techniques
1.5.1.5.1 Slicing and Dicing
1.5.1.5.2 Pivoting
1.5.1.5.3 Drill-Down, Roll-Up, and Drill-Across

1.5.2 The Analytics Landscape
1.5.2.1 Types of Analytics (Descriptive, Diagnostic, Predictive, Prescriptive)
1.5.2.1.1 Descriptive Analytics
1.5.2.1.2 Diagnostic Analytics
1.5.2.1.3 Predictive Analytics
1.5.2.1.4 Prescriptive Analytics
1.6 Conclusion
1.7 Key Terms
1.8 Test Your Understanding
1.9 Read More
1.10 Lab
1.10.1 Introduction to R
1.10.2 Introduction to RStudio
1.10.2.1 RStudio Download and Installation
1.10.2.2 Install a Package
1.10.2.3 Activate Package
1.10.2.4 User Readr to Load Data
1.10.2.5 Run a Function
1.10.2.6 Save Status

1.10.3 Introduction to Python and Jupyter Notebook IDE
1.10.3.1 Python Download and Installation
1.10.3.2 Jupyter Download and Installation
1.10.3.3 Load Data and Plot It Visually
1.10.3.4 Save the Execution
1.10.3.5 Load a Saved Execution
1.10.3.6 Upload a Jupyter Notebook File
1.10.4 Do It Yourself
References
Chapter 2: Statistics
2.1 Overview of the Chapter
2.2 Definition of General Terms
2.3 Types of Variables
2.3.1 Measures of Central Tendency
2.3.1.1 Measures of Dispersion
2.4 Inferential Statistics
2.4.1 Data Distribution

2.4.2 Hypothesis Testing
2.4.3 Type I and II Errors
2.4.4 Steps for Performing Hypothesis Testing
2.4.5 Test Statistics
2.4.5.1 Studentś t-test
2.4.5.2 One-Way Analysis of Variance
2.4.5.3 Chi-Square Statistic
2.4.5.4 Correlation
2.4.5.5 Simple Linear Regression
2.5 Conclusion
2.6 Key Terms
2.7 Test Your Understanding
2.8 Read More
2.9 Lab
2.9.1 Working Example in R
2.9.1.1 Statistical Measures Overview
2.9.1.2 Central Tendency Measures in R
2.9.1.3 ispersion in R
2.9.1.4 Statistical Test Using p-value in R
2.9.2 Working Example in Python

2.9.2.1 Central Tendency Measure in Python
2.9.2.2 Dispersion Measures in Python
2.9.2.3 Statistical Testing Using p-value in Python
2.9.3 Do It Yourself
2.9.4 Do More Yourself (Links to Available Datasets for Use)
References
Chapter 3: Overview of Machine Learning Algorithms
3.1 Introduction
3.2 Data Mining
3.3 Analytics and Machine Learning
3.3.1 Terminology Used in Machine Learning
3.3.2 Machine Learning Algorithms: A Classification
3.4 Supervised Learning
3.4.1 Multivariate Regression
3.4.1.1 Multiple Linear Regression