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

Frequently asked questions


Q: What are some of the common use cases of natural language processing?

A: Natural Language processing is branch of Machine learning algorithms that process text data to produce meaningful insights. A few of the common use cases of NLP are answering questions asked by the user, sentimental analysis, language translation to a foreign language, search engines, and document classifications. The key point to understand here is that if you want to perform analytics/machine learning on data represented by text/sentences/word format, NLP is the way to go.

Q: How is feature extraction relevant to NLP?

A: Machine learning algorithms work on mathematical forms. Any other forms, such as Text, need to be converted into mathematical forms to apply machine learning algorithms. Feature extraction is converting forms, such as texts/images, into numerical features, such as Vectors. These numerical features act as an input to Machine learning algorithms. Techniques such as TF-IDF...