Artificial Intelligence for Business (SpringerBriefs in Business)

✍ Scribed by Rajendra Akerkar

• Publisher: Springer
• Year: 2018
• Tongue: English
• Leaves: 92
• Edition: 1st ed. 2019
• Category: Library

✦ Synopsis

This book offers a practical guide to artificial intelligence (AI) techniques that are used in business. The book does not focus on AI models and algorithms, but instead provides an overview of the most popular and frequently used models in business. This allows the book to easily explain AI paradigms and concepts for business students and executives. Artificial Intelligence for Business is divided into six chapters. Chapter 1 begins with a brief introduction to AI and describes its relationship with machine learning, data science and big data analytics. Chapter 2 presents core machine learning workflow and the most effective machine learning techniques. Chapter 3 deals with deep learning, a popular technique for developing AI applications. Chapter 4 introduces recommendation engines for business and covers how to use them to be more competitive. Chapter 5 features natural language processing (NLP) for sentiment analysis focused on emotions. With the help of sentiment analysis, businesses can understand their customers better to improve their experience, which will help the businesses change their market position. Chapter 6 states potential business prospects of AI and the benefits that companies can realize by implementing AI in their processes.

✦ Table of Contents

Preface
Contents
Introduction to Artificial Intelligence
Data
Information
Knowledge
Intelligence
Basic Concepts of Artificial Intelligence
Benefits of AI
Data Pyramid
Property of Autonomy
Situation Awareness
Business Innovation with Big Data and Artificial Intelligence
Overlapping of Artificial Intelligence with Other Fields
Ethics and Privacy Issues
AI and Predictive Analytics
Application Areas
Clustering or Segmentation
Psychographic Personas
Machine Learning
Introduction
Machine Learning Workflow
Learning Algorithms
Linear Regression
k-Nearest Neighbour
Decision Trees
Feature Construction and Data Reduction
Random Forest
k-Means Algorithm
Dimensionality Reduction
Reinforcement Learning
Gradient Boosting
Neural Networks
Deep Learning
Introduction
Analysing Big Data
Different Deep Learning Models
Autoencoders
Deep Belief Net
Convolutional Neural Networks
Recurrent Neural Networks
Reinforcement Learning to Neural Networks
Applications of Deep Learning in Business
Business Use Case Example: Deep Learning for e-Commerce
Recommendation Engines
Introduction
Recommendation System Techniques
Content-Based Recommendations
Item Representations
User Profiles
Learning of User Models
Collaborative Recommendations
Hybrid Approaches
Applications of Recommendation Engines in Business
Collection of Data
Storing the Data
Analysing the Data
Product Recommendation Algorithm
Business Use Case
Natural Language Processing
Introduction
Morphological Processing
Syntax and Semantics
Semantics and Pragmatics
Use Cases of NLP
Text Analytics
Sentiment Analysis
Sentiment Analysis Use Cases
Challenges of Sentiment Analysis
Applications of NLP in Business
Customer Service
Reputation Monitoring
Market Intelligence
Sentiment Technology in Business
Employing AI in Business
Analytics Landscape
Application Areas
Complexity of Analytics
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
Embedding AI into Business Processes
Implementation and Action
Artificial Intelligence for Growth
AI for Customer Service
Applying AI for Marketing
Glossary
References