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  • Book Overview & Buying Artificial Intelligence for Big Data
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Artificial Intelligence for Big Data

Artificial Intelligence for Big Data

By : Anand Deshpande, Manish Kumar, Albenzo Coletta, Giancarlo Zaccone
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Artificial Intelligence for Big Data

Artificial Intelligence for Big Data

5 (2)
By: Anand Deshpande, Manish Kumar, Albenzo Coletta, Giancarlo Zaccone

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 (14 chapters)
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Applying NLP techniques


Generally, for any class of NLP problems, you first apply text preprocessing and feature extraction techniques. Once you have reduced the noise in the text and are able to extract features out of text, you perform various machine learning algorithms to solve different NLP classes of NLP problems. In this section, we will cover one such problem, called text classification.

Text classification

Text classification is one of the very common use cases of NLP. Text classification can be used for use cases such as email SPAM detection, identifying retail product hierarchy, and sentiment analysis. This process is typically a classification problem wherein we are trying to identify a specific topic from a natural language source of a large volume of data. Within each of the data groups, we may have multiple topics discussed and hence it is important to classify the article or the textual information into logical groups. Text classification techniques help us to do that.

These...

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