Artificial Intelligence for Business

verfasst von: Rajendra Akerkar

Verlag: Springer International Publishing

Buchreihe : SpringerBriefs in Business

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter

Introduction to Artificial Intelligence

Abstract

This chapter provides a brief introduction to artificial intelligence – presents a basic concept of AI and describes its relationship with machine learning, data science, data mining and predictive analytics. The chapter also deals with other related issues such as ethics and privacy.

Rajendra Akerkar

Machine Learning

Abstract

This chapter discusses core machine learning – workflow and the most effective machine learning techniques. Machine learning is the process of teaching a computer system how to make accurate predictions when fed data. After a brief overview of the discipline’s most common techniques and applications, readers will gain more insight into the assessment and training of different machine learning models for business problems.

Rajendra Akerkar

Deep Learning

Abstract

The chapter 3 deals with deep learning – a common technique for developing AI applications. Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. The chapter also presents different deep learning models such as deep convolutional nets that have brought about breakthroughs in processing images, video, speech and audio.

Rajendra Akerkar

Recommendation Engines

Abstract

This chapter introduces recommendation engines – one of the concepts in AI has gained momentum. It is a marketer tool for online businesses. Recommendation engine is seen as an intelligent and sophisticated salesman who knows the customer taste and style and thus can make more smart decisions about what recommendations would benefit the customer most thus increasing the possibility of a conversion. Though it started off in e-commerce, it is now gaining popularity in other sectors, including Media.

Rajendra Akerkar

Natural Language Processing

Abstract

This chapter presents a primer on natural language processing (NLP) – a technique that gives machines the ability to read, understand and derive meaning from the human languages. Businesses are turning to NLP technology to derive understanding from the enormous amount of unstructured data available online and in call logs. The section also explores 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.

Rajendra Akerkar

Employing AI in Business

Abstract

This chapter deals with observations and insight – on employing AI solutions in business. Without finding a problem to solve, business will not gain the desired benefits when employing AI. If they are looking for a solution to detect anomalies, predict an event or outcome, or optimize a procedure or practice, then they have a problem AI can address. The chapter begins with unfolding analytics landscape and describes how to embed AI in business processes. Further, it discusses potential business prospects of AI and the benefits that companies can realize by implementing AI in their processes.

Rajendra Akerkar

Backmatter

Titel: Artificial Intelligence for Business
verfasst von: Rajendra Akerkar
Verlag: Springer International Publishing
Electronic ISBN: 978-3-319-97436-1
Print ISBN: 978-3-319-97435-4
DOI: https://doi.org/10.1007/978-3-319-97436-1