Book Image

Artificial Intelligence for Big Data

By : Anand Deshpande, Manish Kumar
Book Image

Artificial Intelligence for Big Data

By: Anand Deshpande, Manish Kumar

Overview of this book

In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Natural language processing basics


Before we state some of the high-level steps involved in NLP, it is important to establish a definition of NLP. In simple terms, NLP is a collection of processes, algorithms, and tools used by intelligent systems to interpret text data written in human language for actionable insights. The mention of text data makes one fact about NLP very evident. NLP is all about interpreting unstructured data. NLP organizes unstructured text data and uses sophisticated methods to solve a plethora of problems, such as sentiment analysis, document classification, and text summarization. In this section, we will talk about some of the basic steps involved in NLP.

In the subsequent sections, we will take a deep dive into those steps. The following diagram represents some of the basics hierarchical steps involved in NLP:

Let us look at each of these steps briefly:

  • Type of machine learning: NLP can be performed either using supervised learning algorithms or as unsupervised learning...